ADVANCED PROGRAM · 2021. 8. 2. · ADVANCED PROGRAM | ICOICT 2021 3 COMMITTEE Steering Committee § A Adiwijaya, Telkom University, Indonesia § Ahmad Rafi, Multimedia University,

ADVANCED PROGRAM | ICOICT 2021

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CONFERENCEINFORMATIONDates August,3–52021

Organizer TelkomUniversity,Indonesia

Co-organizer MultimediaUniversity,Malaysia

UniversitasGadjahMada,Indonesia

Venue ZoomVirtualWebinarandMeeting

Secretariat SchoolofComputing,TelkomUniversity

PanambulaiBuilding

Jl.TelekomunikasiTerusanBuahBatu,Bandung

WestJava40257

Indonesia

Phone:+62227569131

Fax:+62227565930

Email:[email protected]

ConferenceWebsite www.icoict.org


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WELCOMEMESSAGEOn behalf of the Organizing and Program Committee, we warmly welcome you to the 9thInternational Conference on Information and Communication Technology (ICoICT) 2021 onAugust3-5th,2021.The9th ICoICT2021 is jointlyorganizedbyTelkomUniversity Indonesia,MultimediaUniversityMalaysia,andUniversitasGadjahMadaIndonesia,inassociationwithIEEEIndonesiaSection,IEEESignalProcessingSocietyChapter,andTheIndonesiaSectionComputerSocietyChapter.ThepreviousICoICTconferenceshavesuccessfullyservedasaforumtobringtogetheradiversegroupofpeoplefromacademicsandindustriestoshareandpresentthelatestissuesandrecentdevelopments in InformationandCommunicationTechnology(ICT).PapersfromthepreviousICoICT2013until2020havebeenpublishedinIEEEXploreandindexedinScopus.Thetechnicalprogramof9thICoICT2021consistsofeightkeynotes,oneknowledgetransfer,fivetutorials,sixtrackson“DigitalInnovationsforPost-pandemicRecovery”,and25parallelsessions.Forthefirsttime,theconferencefeaturessocialeventsthatallowconferenceparticipantstomeetanddiscusswithfellowresearchersinthesamefieldofinterest.CompetitionfortheBestPaperAwardisalsoorganized.The9thICoICT2021received296papersubmissionsfrom20countries,outofwhich122papershavebeenaccepted–correspondingtoanacceptancerateof43.4%.Allpapersubmissionshavebeensubjectedtoarigorouspeer-reviewprocessthatevaluatestheirsignificance,novelty,andtechnicalquality.Eachpaperwasreviewedindependentlybyatleastthreeexperts.Due to the COVID-19 pandemic, we have decided to hold the 9th ICoICT 2021 as a virtualconference.Theorganizingcommitteehadbeenworkhardtocreateavirtualconferencethatwillbevaluableandengagingforbothpresentersandattendees.Thefullconferenceformatmixespre-recorded and asynchronous engagement and lives engagement through Question-and-Answer(Q&A).The9thICoICT2021hasbeenorganizedasaresultoftheworkandeffortofcolleagues,friends,andorganizations.Wewishtothankallwhohaveparticipatedandsupportedourworkinmanywaysandallwhohelpedusmakethiseventpossibleandsuccessful.WewouldliketoexpressourgratitudetotheOrganizingCommitteeandTechnicalCommitteemembersandallTelkomUniversitycolleagueswhoassistedusinplanningandorganizingthisconference.Wealsowishtothankallthereviewerswhoworkedveryhardinreviewingpapersandprovidingsuggestionsforthepaper’simprovements.WewouldliketoexpressoursinceregratitudetotheKeynoteandTutorialSpeakers.Wewouldalsoliketothankallofthesponsoringorganizationsforprovidingtheirgenerous financial support.Lastbutnot least,wewould like togiveappreciation to theauthorswhohavesubmittedtheirexcellentworkstothisconferenceandalltheattendees.Weappreciateyourvirtualattendanceat the9th ICoICT2021.Wehopeyouenjoyall thekeynotesessions,thetechnicalsessions,andthesocialeventsandinspireyourfutureresearch.Dr.WarihMaharaniGeneralChairofICoICT2021


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COMMITTEESteeringCommittee

§ AAdiwijaya,TelkomUniversity,Indonesia§ AhmadRafi,MultimediaUniversity,Malaysia§ AfizanAzman,MelakaInternationalCollegeofScienceandTechnologyMalaysia§ AriMoesriamiBarmawi,TelkomUniversity,Indonesia§ HairulA.Abdul-Rashid,MultimediaUniversity,Malaysia§ SongHoeLau,MultimediaUniversity,Malaysia§ MamanAbdurohman,TelkomUniversity,Indonesia§ ParmanSukarno,TelkomUniversity,Indonesia§ RinaPudjiastuti,TelkomUniversity,Indonesia§ ShafinarIsmail,UniversitiTeknologiMara,Malaysia§ SyedAbdulRahmanAlHaddad,UniversitiPutraMalaysia,Malaysia§ KikiMaulanaAdhinugraha,LaTrobeUniversity,Australia§ SultanAlamri,SaudiElectronicUniversity,SaudiArabia

OrganizingCommitteeGeneralChair

§ WarihMaharani,TelkomUniversity,IndonesiaGeneralCo-Chair

§ VeraSuryani,TelkomUniversity,Indonesia§ OngThianSong,MultimediaUniversity,Malaysia

SecretariatChair

§ SitiKarimah,TelkomUniversity,Indonesia§ OoiShihYin,MultimediaUniversity,Malaysia§ DitaOktaria,TelkomUniversity,Indonesia

PublicationChair

§ DawamDwiJatmiko,TelkomUniversity,Indonesia§ AndityaArifianto,TelkomUniversity,Indonesia§ SitiZainabIbrahim,MultimediaUniversity,Malaysia§ Mohd.FikriAzlibinAbdullah,MultimediaUniversity,Malaysia

FinanceChair

§ AnnisaAditsania,TelkomUniversity,Indonesia§ ChongSiewChin,MultimediaUniversity,Malaysia§ SitiSa’adah,TelkomUniversity,Indonesia§ RamantiDharayani,TelkomUniversity,Indonesia

EventandLogisticChair

§ FazmahArif,TelkomUniversity,Indonesia§ MiraSabariah,TelkomUniversity,Indonesia


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§ DonniRichasdy,TelkomUniversity,Indonesia§ AndiDarmawan,UniversitasGadjahMada,Indonesia

CallForPaperChair

§ AgungTotoWibowo,TelkomUniversity,Indonesia§ AuliaKhamasHeikhmakhtiar,TelkomUniversity,Indonesia§ MahmudDwiSulistiyo,TelkomUniversity,Indonesia

TutorialandSpecialSessionChair

§ PutuHarryGunawan,TelkomUniversity,IndonesiaSponsorshipChair

§ KemasRahmatSalehWihaja,TelkomUniversity,Indonesia§ RizkaRezaPahlevi,TelkomUniversity,Indonesia

Webmaster

§ RahmatYasirandi,TelkomUniversity,Indonesia§ MuhammadAlMakky,TelkomUniversity,Indonesia

TechnicalProgramCommitteeTPCChair

§ AdeRomadhony,TelkomUniversity,IndonesiaTrackChair

§ DiditAdytia,TelkomUniversity,Indonesia§ TeeConnie,MultimediaUniversity,Malaysia§ KusumaAyuLaksitowening,TelkomUniversity,Indonesia§ PangYingHan,MultimediaUniversity,Malaysia§ MardhaniRiasetiawan,UniversitasGadjahMada,Indonesia§ DodyQoriUtama,TelkomUniversity,Indonesia

ReviewersAchmadRizal,TelkomUniversityAdeCandra,UniversityofSumateraUtaraAdeRomadhony,TelkomUniversityAdityaFirmanIhsan,TelkomUniversityAfridaHelen,UniversitasPadjadjaran(Unpad)AgungDewandaru,BandungInstituteofTechnologyAgungWibowo,TelkomUniversityAgusHartoyo,TelkomUniversityAlbertusJokoSantoso,UniversitasAtmaJayaYogyakartaAlfanWicaksono,UniversitasIndonesiaAlfianAkbarGozali,TelkomUniversityAmarilisPutriYanuarifiani,MultimediaUniversityAndiWahjuRahardjoEmanuel,UniversitasAtmaJayaYogyakartaAndryAlamsyah,TelkomUniversityAngelinaKurniati,TelkomUniversity


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ApriSiswanto,UniversitasIslamRiauArbiNasution,UniversitasIslamRiauAriBarmawi,TelkomUniversityArieArdiyanti,TelkomUniversityArnidaLatifah,IndonesianInstituteofScienceAsepWahyudin,UniversitasPendidikanIndonesia(UPIAshokKumarVeerasamy,UniversityofTurkuBayuErfianto,TelkomUniversityBedyPurnama,TelkomUniversityBerliyanto,InstitutTeknologiBudiUtomoBonyParulianJosaphat,SekolahTinggiIlmuStatistikBudiPrasetya,TelkomUniversityCatherineSereati,UniversitasKatolikIndonesiaAtmaJayaCeciliaEstiNugraheni,ParahyanganCatholicUniversityChinPooLee,MultimediaUniversityChooKimTan,MultimediaUniversityChristianNaa,ParahyanganCatholicUniversityChristinaJuliane,STMIKAMIKBANDUNGDadeNurjanah,TelkomUniversityDanaSulistyoKusumo,TelkomUniversityDanySaputra,UniversitasBinaNusantaraDedyRahmanWijaya,TelkomUniversityDeniSaepudin,TelkomUniversityDerwinSuhartono,BinaNusantaraUniversityDhomasHattaFudholi,UniversitasIslamIndonesiaDianaPurwitasari,InstitutTeknologiSepuluhNopemberDidiRosiyadi,IndonesianInstituteofSciencesDiditAdytia,TelkomUniversityDodyUtama,TelkomUniversityDumaHutapea,UniversitasKatolikIndonesiaAtmaJayaJakartaDwizaRiana,STMIKNusaMandiriJakartaEmaRachmawati,TelkomUniversityEmilKaburuan,BinaNusantaraUniversityEndahSudarmilah,UniversitasMuhammadiyahSurakartaEndangChumaidiyah,TelkomUniversityEndangDjuana,TrisaktiUniversityErnaNababan,UniversityofNorthSumateraErwinB.Setiawan,TelkomUniversityEsaPrakasa,IndonesianInstituteofSciencesEvizalAbdulKadir,UniversitasIslamRiauFakhriyHario,UniversitasBrawijayaFang-FangChua,MultimediaUniversityFarahAfianti,TelkomUniversityFarizDarari,UniversitasIndonesiaFavianDewanta,TelkomUniversityFazmahArifYulianto,TelkomUniversityFettyLubis,BandungInstituteofTechnologyFordGaol,BinaNusantaraUniversityGandevaBayuSatrya,TelkomUniversityGentaIndraWinata,HongKongUniversityofScienceandTechnologyGoHasegawa,TohokuUniversityGohKahOngMichael,MultimediaUniversity


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HapnesToba,MaranathaChristianUniversityHarrySantoso,UniversitasIndonesiaHengSiongLim,MultimediaUniversityHilalNuha,TelkomUniversityIGustiBagusBaskaraNugraha,BandungInstituteofTechnologyImeldaAtastina,TelkomUniversityIndraBudi,UniversitasIndonesiaIndraChandra,TelkomUniversityInnaSyafarina,IndonesianInstituteofScienceInneHusein,TelkomUniversityIntanNurmaYulita,UniversitasPadjadjaranIrwanIrwan,STMIKMDPIsmanKurniawan,TelkomUniversityIzzatdinAbdulAziz,UniversitiTeknologiPETRONASJanWiraGotamaPutra,TokyoInstituteofTechnologyJessicaPermatasari,MultimediaUniversityJi-JianChin,MultimediaUniversityJimmyTirtawangsa,TelkomUniversityKarelBachri,AtmaJayaCatholicUniversityofIndonesiaKemasMuslimLhaksmana,TelkomUniversityKemasRahmatSalehWiharja,TelkomUniversityKianMingLim,MultimediaUniversityKikiMaulanaAdhinugraha,LaTrobeUniversityKorediantoUsman,TelkomUniversityKurnianingsihKurnianingsih,PoliteknikNegeriSemarangKusumaLaksitowening,TelkomUniversityLeeYingChong,MultimediaUniversityLee-YengOng,MultimediaUniversityLeonardGoeirmanto,MercuBuanaUniversityLeowMengChew,MultimediaUniversityLillianYeeKiawWang,MonashUniversityMalaysiaMahmudSulistiyo,TelkomUniversityMamanAbdurohman,TelkomUniversityMarisaParyasto,TelkomUniversityMasayuLeyliaKhodra,InstitutTeknologiBandungMewatiAyub,MaranathaChristianUniversityMickeRusmerryani,ResearchLabAPChannelMiraKaniaSabariah,TelkomUniversityMochArifBijaksana,TelkomUniversityMohammedS.Al-Abiad,UniversityofBritishColumbiaMuhamadKoyimatu,UniversitasPertaminaMuhamadRisqiUtamaSaputra,UniversityofOxfordMuhammadAgniCatur,BhaktiSampoernaUniversityMuhammadHaris,BukalapakMuhammadJohanAlibasa,TelkomUniversityMuhammadKusban,UniversitasGadjahMadaMuhammadNurKholishAbdurrazaq,InstitutAgamaIslamAz-ZaytunMustafaMatDeris,UniversityTunHusseinOnnMalaysiaNianChiTay,MultimediaUniversityNikenDwiWahyuCahyani,TelkomUniversityNiruwanTurnbull,MahasarakhamUniversityNoorCholisBasjaruddin,PoliteknikNegeriBandung


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NungkiSelviandro,TelkomUniversityNurulAin,UniversitiTeknologiMaraOngThianSong,MultimediaUniversityOpimSitompul,UniversityofNorthSumateraPancaPutra,UniversitasIndonesiaParmanSukarno,TelkomUniversityPaulusInsapSantosa,UniversitasGadjahMadaPeyYunGoh,MultimediaUniversityPutuHarryGunawan,TelkomUniversityRadoslavaKraleva,South-WestUniversityRayAdderleyJMGining,UniversitiTeknologiMARARezaPulungan,UniversitasGadjahMadaRimbaWhidianaCiptasari,TelkomUniversityRinaMardiati,UINSunanGunungJatiRioGunturUtomo,TelkomUniversityRisnandar,IndonesianInstituteofSciencesRobertusNugroho,SoegijapranataCatholicUniversityRonaldAdrian,UniversitasGadjahMadaRonyKalfarisi,BentleySystemsRuhailaMaskat,UniversitiTeknologiMARASandyVantika,BandungInstituteofTechnologySaparudinSaparudin,TelkomUniversitySariDewiBudiwati,TelkomUniversitySatriaMandala,TelkomUniversitySeptafiansyahDwiPutra,PoliteknikNegeriLampungSetiawanHadi,UniversitasPadjadjaranSetyorini,TelkomUniversityShenYeePang,MultimediaUniversityShihYinOoi,MultimediaUniversityShingChyiChua,MultimediaUniversityShukorSanimMohdFauzi,UniversitiTeknologiMara,PerlisCampusSiewChinChong,MultimediaUniversitySinYinTan,MultimediaUniversitySiongHoeLau,MultimediaUniversitySitiFatimahAbdulRazak,MultimediaUniversitySitiZainabIbrahim,MultimediaUniversitySritrustaSukaridhoto,PoliteknikElektronikaNegeriSurabayaSusetyoBagasBhaskoro,PoliteknikManufakturBandungSuyanto,TelkomUniversitySwee-HuayHeng,MultimediaUniversityTajulRosliRazak,UniversitiTeknologiMARATantyOktavia,BinaNusantaraUniversityTedjoDarmanto,STMIKAMIKBandungTeeConnie,MultimediaUniversityThomasBasuki,CurtinUniversityThomhertSiadari,DDHInc.TimilehinAderinola,MultimediaUniversityTiranaNoorFatyanosa,BrawijayaUniversityTohariAhmad,InstitutTeknologiSepuluhNopemberToraFahrudin,TelkomUniversityVeraSuryani,TelkomUniversityWarihMaharani,TelkomUniversity


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WarsunNajib,UniversitasGadjahMadaWeeHowKhoh,MultimediaUniversityWeiChuenYauXiamen,UniversityMalaysiaWikkyAlMaki,TelkomUniversityWisnuAnantaKusuma,BogorAgricultureInstituteWiwinSuwarningsih,IndonesianInstituteofScienceYantiRusmawati,TelkomUniversityYeePingLiew,MultimediaUniversityYeeYenYuen,MultimediaUniversityYingHanPang,MultimediaUniversityYudhaPurwanto,TelkomUniversityYudiWibisono,IndonesiaUniversityofEducationYuliantSibaroni,TelkomUniversityYunitaSari,UniversitasGadjahMadaZ.k.AbdurahmanBaizal,TelkomUniversityZoyaPourmirza,NewcastleUniversity


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VIRTUALCONFERENCEGENERALINSTRUCTIONS

GeneralInformationICoICT2021will be hosted as a fully virtual conferenceusing Zoom.There are four types ofsessionsinICoICT2021,namely:keynotespeeches,tutorials,Birds-of-a-feather(BoF)sessions,andpaperpresentationsessions.Eachsessionwillhaveaco-host,amoderatororsessionchairorsessionhost,presenters,andattendees.Theco-hostwillbethepersonthatfacilitatestheuseof the technology. The moderator or session chair is present to coordinate the session andmanageQ&A.Accesstosessions• Keynotespeechesandtutorialswillbeheld in theZoomWebinarroomwhichhasa large

enoughcapacityandwillalsobelivestreamedviaYoutubesothatitcanbefreelyaccessedbythepublic.

• Birds-of-a-feathersessionsandparallelpaperpresentationswillbeheldintheZoomMeetingroomswhicharesmallerincapacityandcanonlybeattendedbypresentersandauthorsofregisteredpapers.

BasicSessionStructure• 10minutesbeforetheSession:Theco-hostwillstartthedesignatedZoomsession.• 2minutesbeforetheSession:Theco-hostwillstarttherecording.• 1minutebeforetheSession:Themoderatororsessionchairintroducesthesession.• BeginningoftheSession:Themoderatororsessionchairwillintroducethepresenter.

ThetalkwillbepresentedusingthevideorecordingsubmittedbythepresenterfollowedbyliveQ&A.Thedurationofeachpresentationbytypearelistedbelow:

o Keynotespeech:(1hour)Recordedpresentation(video)=40minutesLiveQ&A=10minutes

o Tutorial:(1.5hours)Recordedvideo=40–60minutesLiveQ&A=20-40minutes

o Birds-of-a-feather:(2–2.5hours)o Paperpresentation:(20minutes)

Recordedpresentation(video)=15minutesLiveQ&A=5minutes

• EndoftheSession:Themoderatororsessionchairwillconcludethesessionandcheckattendance,thentheco-hostwillendthesession.

ScheduleandTimeAlltimeintheprogramscheduleisinWesternIndonesiaTime/WaktuIndonesiaBarat(WIB;UTC+7).Pleasemakeadjustmentstothetimestatedinthescheduleintoyourlocaltime.CurrenttimeinWIBcanbefoundonhttp://time.bmkg.go.id/.


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CONFERENCESCHEDULE

08.15–08.30 SoftOpeningbyDeanofSchoolofComputing,TelkomUniversity

(Dr.Z.K.AbdurahmanBaizal)

08.30–09.30 KeynoteSpeech1:Prof.HaryadiS.Gunawi(UniversityofChicago)WhyDoestheCloudStopComputing?LessonsfromThousandsofBugReports,ServiceOutages,andAnecdotalEvidence

09.30–11.30 Tutorial1:JuneidiTsai(MicrosoftIndonesia)MicrosoftEducationTransformationFrameworkforHigherEducation

11.30–12.30 Break

12.30–13.30 KeynoteSpeech2:Dr.DavidTaniar(MonashUniversity)ContactTracingduringCovid-19Pandemic:AnAustralianExperience

13.30–14.30 KeynoteSpeech3:SolehAyubi,Ph.D(ChiefDigitalHealthcareOfficeratBioFarmaIndonesia)Covid-19VaccineDistribution:anexampleofAcceleratinghealthcaretransformation

14.30–14.45 Break14.45–17.45 BirdsofaFeatherSessions

RoomYudhistira:Dr.MayaAriyanti

"ELearning"

RoomBima:Afiahayati,Ph.D"AIandBioinformaticsforCOVID-19"

RoomArjuna:KemasRahmatShaleh,Ph.D"KnowledgeManagement,KnowledgeRepresentationandreasoning"

RoomNakula:Dr.AgusHartoyo"ArtificialIntelligenceofThings(AIoT):TheintersectionofAIandIoT"

RoomSadewa:Dr.MuhammadJohanAlibasa“DigitalfootprintsduringCOVID-19pandemics”

Day 1, Tuesday 3 August 2021


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CONFERENCESCHEDULE08.00–08.10 OpeningCeremony:Greetingsandvideopresentation08.10–08.20 WelcomeSpeechbyGeneralChairofICoICT2021

Dr.WarihMaharani08.20–08.25 SpeechbyDeanoftheFacultyFacultyofMathematicsandNaturalScience,

UniversitasGadjahMadaProf.Dr.Triyono,S.U.

08.25–08.30 Speech by Dean of the Faculty of Information Science and Technology,MultimediaUniversityProf.Ts.Dr.LauSiongHoe

08.30–08.35 SpeechbyChairofIEEEIndonesiaSectionDr.-IngWahyudiHasbi,S.Si,M.Kom

08.35–08.40 OpeningSpeechbyRectorofTelkomUniversityProf.Dr.Adiwijaya

08.40–08.45 PhotoSessions

08.45–09.45 KeynoteSpeech4:Dr.Ir.HammamRizaM.Sc,IPU(BPPT)DigitalInnovationsforPost-PandemicRecovery

09.45–10.45 KnowledgeTransfer:WuShiwei(HUAWEICLOUD-CTOofAPACRegion)WorkforceEnablementintheNewNormalofPost-Pandemic,PoweredByCloudTechnology

10.45–11.00 Break11.00–13.00 Tutorial2:Dr.BayuErfianto(TelkomUniversity)

Chest-wallmotiontrackingusingInertialMeasurementUnits(IMUs)

Parallelsessions#1

13.00–14.00 Break14.00–16.00 Tutorial3:MohEdiWibowo,S.Kom.,M.Kom.,Ph.D.(UGM)

Faceanalysisinpublicspace

Parallelsessions#2

16.00–17.00 KeynoteSpeech5:Prof.Dr.KazemRahimi(TheUniversityofOxford)Machine learning and digital technologies in the context ofepidemiologyandclinicaltrials

17.00–18.00

Keynote Speech 6: Dr. Mohamad Hardyman Barawi (Universiti MalaysiaSarawak)ModellingsentimentandcontrastiveopinionofCOVID-19PandemiconSocialMedia:LinkingComputerScienceandSocialScience

18.00–18.10 Closingday2

Day 2, Wednesday 4 August 2021


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CONFERENCESCHEDULE

08.00–08.15 Openingday308.15–10.15 Tutorial4:DhanySaputra,Ph.D(Brüel&KjærVibro)

Bioinformaticshashelpedprevent&controlapandemic.What'snext?

Parallelsessions#3

10.15–12.15 Tutorial5:Assoc.Prof.Dr.Md.ShohelSayeed(MMU)BigData:Trends,Challenges&Opportunities

Parallelsessions#4

12.15–13.15 Break

13.15–15.15 ParallelSessions#5

15.15–15.30 Break

15.30–16.30 KeynoteSpeech7:Prof.HadiSusanto(UniversityofEssex)COVID-19modellinginIndonesia:Amathematician’sapology

16.30–17.30 KeynoteSpeech8:RezaKhorshidi,D.Phil.((TheUniversityofOxford)

17.30–17.35 Greetings17.35–17.45 BestpaperawardbyICoICT2021TPCChair

Dr.AdeRomadhony

17.45–17.50 ICoICT2022presentation

17.50–17.55 ClosingbyViceDeanofSchoolofComputingTelkomUniversityParmanSukarno,Ph.D

Day 3, Thursday 5 August 2021


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INSTRUCTIONFORMODERATORS

ANDSESSIONCHAIRS

• Generalresponsibilityofamoderatororsessionchair:

1. Introduce the session, explain the structure of the session, rule of theQ&A, and then

introduceeachspeakerorpresenter/paper-titlebeforethevideoplayback;

2. TakechatquestionsfromQ&Apanelandchatpanelduringthepresentationplayback;

3. Read the questions to the presenting speaker or author during the Q&A periodwith

absolutelynodelayontheallocatedQ&Aslots;

4. Concludethesessionandcheckattendance;

5. Makesurethetimeisnotviolated

• Organizerwill sendyouan invitation linkviaemail. Justclickon the link to join theZoom

session.

• Pleasejointhesessionatleast10minutesinadvance,pleasetestyourmicrophoneoncejoined

sothatthesessioncanstartontime.

• We recommend that you turn on your video to engage the attendees during the sessional

introduction.

• Afterintroducingthesessionandthepresenter,werecommendyoutoturnoffthevideoso

thattheattendeescanfocusonthetalkvideo.

• Asorganizers,wewouldliketoensureasmoothandproductivevirtualconference.

• Duringthereplayofpresentationvideo,pleasekeeptrackofthequestionsontheQ&Aand

chatpanel.

• Afterthevideoplayback,unmuteyourselfandshareyourwebcam.Thehostwillalsounmutes

andsharescorrespondingspeaker’sorpresenter’swebcam.

• PleasemakesuretoaskorallythequestionsandaccordingtotheFIFOtimetheywerefirst

submitted.Iftherearenotmanyquestions,feelfreetoasksomeofyourown.

• Sometimes the audience may need to clarify their question. In that case, it is upon the

discretion of the Session Chair to unmute the attendeewho placed the question tomake

clarifications.

• PleasebemindfuloftheQ&Atimelimits!Wecannotintroducedelaysonthepredetermined

slotsofeachsession,itwillpushothersessionsbehind.

• Afterthepresenteraddressedaquestion,pleaseindicateitisansweredverbally.

• Thescreenwillcontaincuestohelpyougoverntheflowofthesession.


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INSTRUCTIONFORPRESENTERS

• Organizerwillsendyouaninvitationlinkasapresenterviaemail.Justclickonthelinktojoin

theZoomsession.

• Pleasejoinatleast5minutesbeforeyourdesignatedtimeslotandbepresentduringthevideo

playbackaswellastheQ&Asession.

• Asorganizers,wewouldliketoensureasmoothandproductivevirtualconference.Following

thevideopresentationthereisashortQ&Asession.

• Yourmicrophonewillbemutedduringthevideoplayback.

• Duringthereplayofthepresentationvideo,attendeesorotherpresenterswillaskquestions

related to the presentation through theQ&Apanel (for attendee) or chat panel (for other

presenters).

• You are encouraged to keep an eye on the questions so to answer them during the Q&A

Session.

• Attheendofthepre-recordedpresentation,thehostwillunmuteyourmicrophoneandshares

your webcam. The moderator or session chair will then ask you to answer some of the

questionsinsequencetheyweresubmittedandwithintheallottedQ&Aperiod.

• Themoderatororsessionchairwilltrytocoverasmanyquestionsaspossibledependingon

theallottedtime.Incase,someofthequestionshavenotbeenansweredattendeesmaywant

todiscusswiththepresenteroff-line.

• Youarewelcometostayinthesessionasanattendeewhennotpresenting.

• Whenyouarenotpresentingyourpaper,youcanalsoaskquestionstootherpresentersvia

chatpanel.Beginyourtextwith“[ASK]”toindicateitasaquestiontothepresenter.Senditto

sessionchair(orallpanelistifyouprefertodoso).

• Youarealsowelcometoregisterasanattendeeforanyothersession/eventthatinterests

you (including keynote speeches, tutorials, Birds-of-a-feather sessions, and other paper

presentations)usingregistrationlinksprovidedintheicoict.org.Pleasenotethateachroom

haslimitedcapacity.

Breaks

Conferencing, online and in-person can be exhausting!Weneed to take breaks.Wewill take

breaks.Breaksarebuilt-intotheschedule!

• Standupandstretch,getasnack,comebackrefreshed!

• IfyouleaveyourZoomon,makesurethatyourmicrophoneismutedduringthebreak.


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INSTRUCTIONFORBoFSESSIONHOSTS

• The main responsibilities of a BoF host are opening a live session on Zoom, welcoming

participantstoaBoFsession,andstartingandkeepingtheconversationgoing.

• GeneralguidanceforaBoFsessionhost:

1. Introducethesession,explainthestructureofthesessionandruleofthediscussion.

2. BoF session may include a short presentation from the host to introduce important

questionsorthemesofBoFtopic.

3. SharesomeinterestingquestionsandkeyresearchpapersrelatedtotheBoFtopic.

4. ThemainpurposeofBoFsessionisdiscussionandnetworking.Encourageparticipantsto

sharetheircontactinfoandresearchinterests.

5. BoFsessionscanbeformalorinformalasthehostprefers.

6. Concludethesession.

7. Makesurethetimeisnotviolated

• Organizerwill sendyouan invitation linkviaemail. Justclickon the link to join theZoom

session.

• Pleasejointhesessionatleast10minutesinadvance,pleasetestyourmicrophoneoncejoined

sothatthesessioncanstartontime.

• We recommend that you turn on your video to engage the attendees during the sessional

introduction.



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INSTRUCTIONFORATTENDEES

• Youarewelcometoregisterforanysession/eventthatinterestsyouusingregistrationlinks

providedintheicoict.org.Pleasenotethateachroomhaslimitedcapacity.

• Webinarsaredesignedtobein"listen-only"mode,sobydefaultallwebinarattendeesare

mutedbytheorganizer.


• FollowingthevideopresentationthereisashortQ&Asessionmoderatedbythesession

chair.

• Duringthereplayofpresentationvideo,youarewelcometoaskquestionsusingtheQ&A

panel.

• ThemoderatororSessionChairwillselectandaskthequestionsduringtheQ&Asession.We

understandthattheremaybenottimetoaskallthequestions.TheChairwillmakesureto

askcomplementaryquestionsandhopefullyaccordingtothetimetheywerefirstsubmitted.

• Theorganizerwillhavetheabilitytounmuteparticipantsifthisisneededtoelaborateon

theirquestions(pleasenotethatduetothelimitedQ&Aduration,attendeesmaywantto

discusswiththeauthor(s)off-line).

• YoudonotneedtoannounceyourselfarrivingorleavingaSession.

Breaks

Conferencing,onlineandin-personcanbeexhausting!Weneedtotakebreaks.Wewilltake

breaks.Breaksarebuilt-intotheschedule!

• Standupandstretch,getasnack,comebackrefreshed!

• IfyouleaveyourZoomon,makesurethatyourmicrophoneismutedduringthebreak.


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KEYNOTESPEAKERS

Dr.Ir.HammamRizaM.Sc,IPU

HeadofAgencyfortheAssessmentandApplicationofTechnology(BPPT)

Profile

Dr. Ir. Hammam Riza currently hold position as DeputyChairman for Natural Resource Technology Development(TPSA) at BPPT and Vice President of ASEAN ChiefInformation Officer Association (*ACIOA). Currentlyundertaking Bigdata and IoT project for Earth and OceanScience Technology including Agricultural productivitymapping (KSA) and Waste to Energy pilot project (PLTSa)

He founded and hold the position of Executive ChiefInformation Officer (CIO) in conjunction with serving asDirector of Center for Information and CommunicationTechnology (PTIK) at the Agency for the Assessment andApplicationofTechnology–BPPT,GovernmentofIndonesia,

deliveringcuttingedge ICT innovativesolution toministries, regionalgovernmentsandstate-ownedenterprises.Title:DigitalInnovationsforPost-PandemicRecoveryAbstractTheCovid-19pandemicisahealthcrisisthataffectsallaspectsofpeople'slives,especiallytheeconomicaspect.WehavenowgottenusedtotheNewNormalera,ofcourse,withahighlevelofvigilance.Allcountriestodayarefacedwiththedilemmaofhowtorestoresocio-economiclife,inthemidstofeffortstostopthespreadofCovid-19.HandlingCOVID-19inIndonesiainvolves3importantrolesfromstakeholders,namely:Government,Society,andTechnologyasenablerstofacilitatetheprocessofTesting,Tracing,Isolating,toTreatment.Thegovernmentcontinuestostrive to prepare some strategies for handling Covid-19, one of which is by establishinginstitutionalsynergiesinthe'TaskForceforResearchandTechnologicalInnovationforHandlingCovid-19 (TFRIC-19) led by the Agency for the Assessment and Application of Technology(BPPT).DigitalTransformationisthekeytonationaleconomicrecoveryandhelpsdeliverthe“newnormal”adaptation.ThisisinlinewiththePresident'smandatetoexpandaccess,improveinfrastructure,acceleratethedigitaltransformationroadmapanditsregulations,includingthepreparation of digital talents. Another aspect that can also encourage increased economicrecovery is the aspect of leadership through synergy and collaboration of all related entities.BPPTandTFRIC-19haveestablishedasystematicandconstructiveecosystemrelatedtotestingtechnology innovations andmedical devices, including: the testing strategy is carried out bydesigningtheRT-PCRtestkitwhichisthegoldenstandardforCovid-19testing,thentheCovid-19Monitorapplicationisdesignedwhichcanmapthemovementofthevirusthroughtracingsuspectsascarriers.Theincreaseintestingneedsisanticipatedbybuildingalevel-2biosafety


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mobile labequippedwithcomplete test instruments.Strengthening thepreventionprocess isalsocarriedoutbypreparingappropriatetechnologyfacilitiesandinfrastructureintheformofinnovativehandwashingtoolsanddisinfectantvariants.IntherealmofTreatment,anAI-basedMedical ImageManagementdiagnostic toolhasbeenprepared toprocessX-RayandCT-Scandata,whichcanbeasolutionfortheunevendistributionofhealthtoolsandtechnologythatcanbecome an obstacle in the speed of establishing patient diagnoses under supervision. In thecurativeaspect,especiallyforCovidpatientswithseveresymptomswhorequirerespiratoryaids,3variantsofemergencyventilatorsareprepared,asneeded.Inthefuture,theplanformappingtheentirevirusvarianttoidentifyvirusprofilingwillbecarriedoutthroughpatternrecognition.Thisnationalgenomeresearchwilllaterbecomeavirtualassetforthedevelopmentofbigdataandsupercomputingtobeabletocarryouttheprocessofmonitoringdatacollection,mitigationand produce recommendations and strategic actions that will encourage the emergence ofproduct innovations for handling Covid-19 and awaken national independence in the healthsector. TRFRIC-19 Next generation will continue to be committed through 5 main actions,including:Actiontostrengtheneconomicandtechnologicalstudieswhichincludesupplychainstudies, supply demand studies, pre-commercialization studies, manufacturing industryreadiness studies, TKDN studies, technology audits and innovation ecosystem map studies,Medicaldevicetechnologyinnovationactions,includingICUventilatorinnovations,DirectDigitalRadiography(DDR),quantitativeantibodylevelsmeasuringkitsandAntigenRapidTests,Healthsupplement technology innovation actions, including fermented garlic-based healthsupplements, beta glucan-basedhealth supplements and supplements in the formofnutrientdense biscuits. Actions to strengthen scientific data and applications of artificial intelligence,including AI Application Innovations for Covid-19 detection, Bioprospection Database ofMedicinal Plants, Microbes and Compounds with Potential Drugs for Covid-19 and otherinfectious diseases, as Artificial Intelligence Data Sets, Actions to strengthen Cooperation,CommercializationandMedia.Thisactivityisexpectedtoprovideapositiveestuaryforproductinnovationactionsdevelopedbytheinnovationecosystem.TocreateanindependentIndonesiaandhelpeconomicrecoveryduringtheCovid-19pandemic.


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KEYNOTESPEAKERS

Prof.Dr.KazemRahimiProfessorofCardiovascularMedicineatTheUniversityofOxford

Profile

KazemRahimiisaProfessorofCardiovascularMedicineandPopulationHealth,attheUniversityofOxfordandaconsultantcardiologistattheOxfordUniversityHospitalsNHSTrust.Hisresearch interests include hypertension, heart failure,multimorbidityandcardiovascularriskmanagement,usingavariety of methodologies such as individual-patient meta-analysis, large-scale decentralised clinical trials, and digitalhealth technologies. Kazem leads the Deep Medicineprogramme at the Nuffield Department of Women’s andReproductiveHealthwithamajorinterestintheapplicationofmachinelearningapproachestoelectronichealthrecords.Healso leads theBloodPressureLoweringTreatmentTrialists’

Collaboration(BPLTTC),whichisaninternationalcollaborationofallthemajortrialsofbloodpressureloweringdrugs.HeistheDirectoroftheMartinSchoolProgrammeonInformalCitiesandaCo-InvestigatorofthePEAK-Urbanprogramme.Title:MachinelearninganddigitaltechnologiesinthecontextofepidemiologyandclinicaltrialsAbstract Applications of machine learning/AI have given solutions to themost intractable healthcarechallengesanditisalsochangingfundamentallythewaywepracticemedicine.Inthistalk,wediscuss themachine learning from the perspective of large-scale epidemiology. Dataset fromelectronichealthrecordsallowustodiscoverhiddenassociationsbetweendiseases.Inacaseofvalvularheartdisease,itisconsideredasa“degenerative”condition.Bloodpressureisfoundtohaveastrongrelationshipwithvalvularconditions,similartowhathasbeendescribedformajorvascular diseases. The promise of machine learning and digital health is to use analyticalapproachesforhighdimensionaldataset.Machinelearningandhealthcareprofessional’sjobcanbecategorizedintothreetypes,i.e.,Diagnosis,wherewepredictwhatproblemthatthepatientcurrentlyhas,Prognosis,wherewepredictthecourseofdiseaseforapatient,andIntervention,wherewepredicthowapatientwillrespondtoanintervention.Thosearesimilarwithaimsinthemachinelearning;patternrecognition,forecasting,andcausalprediction,respectively.Oneofthe great promises of applyingmachine learning to clinical data is thepossibility of learningoptimal per-patient treatment rules or termed as personalized medicine. There are threecommonmisconceptionsabout‘treatmentfailure’;1.Confusionoflowincidenceconditionswithloweffectivenessof interventions,2.Confusionofdeterministiccausal linkswithprobabilisticmulticause nature ofmost conditions, and 3.Misunderstanding of individual risk and unduecriticism towards average treatment effects. Especially for the third misconception, averagetreatmenteffectsaretheproblemandweneedpersonalizedriskmodelsforbettermanagement.


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Individualwilleitherexperienceornotexperiencetheoutcome;so,ariskcannotbedeterminedfortheindividual,modelswillprovideanestimateoftruerisk.Unfortunately,noamountofdataormodelling technique can fix this problem and provide the true risk of an outcome for anindividual,buttheycanbebetteratdiscriminatinggroupsofindividualsbyidentificationofmoremeaningful subgroups or stratification. In this case, we need risk prediction for stratifiedmedicine. When interactions are found they are not reliable and usually only generate ahypothesisforfurthertesting.Thereareopportunitiesformachinelearningforbetterstratifiedmedicine. For low-incident events, risk distribution is skewed, which means that averagetreatmenteffectsmaynotreflecttheexperienceofthetypicalpatient.Moreover,averageeffectsmightbedeterminedbyaminorityofhigh-riskpatients.


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KEYNOTESPEAKERS

Dr.DavidTaniarAssociateProfessorDepartmentofSoftwareSystems&Cybersecurity,Monash

University

Profile

DavidTaniarreceivedhisMScandPhDinComputerScience,from Swinburne University of Technology and VictoriaUniversity,respectively.HisresearchisintheareaofBigDataManagement,coveringthe3VsofBigData(Volume,Variety,and Velocity). In Big Data Volume, he works on paralleldatabases,inwhichhehaspublishedabookinthistopic(HighPerformanceParallelDatabaseProcessing,Wiley2008).InBigData Variety, he works on various data structures for datawarehousing, especially for non-relational data. And in BigDataVelocity,heworksonIoTdataprocessing,wherehehascompleted IoT projects for manufacturing, railway,environment and ecology, utility, and healthcare. He haspublishedmore than150 journalpapers invariousareasof

datamanagement.HeistheFoundingEditor-in-ChiefoftwoSCI-Ejournals(DataWarehousingand Mining, and Web and Grid Services). He is currently an Associate Professor at MonashUniversity,Australia.Title:ContactTracingduringCovid-19Pandemic:AnAustralianExperienceAbstractContactTracingistheactivityofretrievinghistoricalactivitiesandtripsforapersonwherehispresenceataspecificlocationmightaffectotherpersonswithinacertainradius.Inrelatedtoacontagiousdisease,aninfectedpersonmightspreadthepathogenstothenearbypeopleduringclosecontactthatcantriggerachainreactionofcommunitytransmission.Thebiggestprobleminobtainingthehistoricalactivitiesinacontacttracingprocedureisprivacyandsecurityissues.Theprivacyissuereferstoprivate-relatedsensitiveinformationthatisnotmeanttobesharedwithanyone.However,duringacontacttracinginvestigation,theauthoritieshavetherighttoknoweverydetailfromasuspectedpatient.Thesecurityissuereferstothesafetyofthesharedprivateinformationtotheauthority.Duetotheseissues,manypatientsarereluctanttosharetheir past activities to the authority. This conditionmakes it even harder to obtain the rightinformationfromthepatients.Thenextconsequenceisthatthespreadingofthediseaseswillbeoff the radar since contact tracing could not be done correctly. Several methods have beenproposedtohelpcontacttracingprocedures.Ingeneral,therearetwotypesofcontacttracingmethods, proximity-based and trajectory-based. While the proximity-based method lackshistoricaltripsandsuffersfrommulti-platformscommunicationissues,trajectory-basedsuffersfromprivacyissues.Thisspeechwilldiscussthesemethodstogetherwiththeirprosandcons.Inconclusion, amethod that canpreserveprivacy and retain the details of the tripwill also beexplainedinthissessionasanalternativemethodtosupportcontacttracing.


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KEYNOTESPEAKERS

Prof.HaryadiS.Gunawi,Ph.D

UniversityofChicago

Profile

HaryadiS.GunawiisanAssociateProfessorintheDepartmentof Computer Science at the University of Chicago where heleadstheUCAREresearchgroup(UChicagosystemsresearchon Availability, Reliability, and Efficiency). He received hisPh.D. inComputerScience fromtheUniversityofWisconsin,Madison in 2009. He was a postdoctoral fellow at theUniversity of California, Berkeley from 2010 to 2012. Hiscurrent research focuses on cloud computing reliability andnew storage technology.He has won numerous awardsincluding NSF CAREER award, NSF Computing InnovationFellowship, Google FacultyResearchAward,NetAppFacultyFellowships, and Honorable Mention for the 2009 ACM

DoctoralDissertationAward.His research focus is in improvingdependabilityof storageandcloudcomputingsystemsinthecontextof(1)performancestability,whereinheisinterestedinbuildingstorageanddistributedsystemsthatarerobusttolatencytailsand"limping"hardware,and (2) reliability and scalability, wherein he is interested in combating concurrency andscalabilitybugsincloud-scaledistributedsystems,and(3)interactionsofmachinelearningandsystems, specifically howmachine learning techniques can address operating/storage systemproblems.Title:WhyDoestheCloudStopComputing?Lessons fromThousandsofBugReports, ServiceOutages,andAnecdotalEvidenceAbstract:Cloudcomputing,“thepracticeofusinganetworkofremoteservershostedontheInternettostore,manage,andprocessdata,ratherthanapersonalcomputer,”hasfundamentallychangedtheway societyperformsdailybusinesses and social activities. Emails, text andvideo chats,pictureandvideosharing,blogsandnews,areallbackedbyalargecomplexcollectionofInternetservices,whichwereferas“theCloud.”Asdependencyoncloudcomputing increases,societydemandshighavailability,anideal24/7serviceuptimeifpossible.Yet,serviceoutagesarehardtoescapefrom. Notonlydooutageshurtcustomers, theyalsocause financialandreputationdamages.Minutesofservicedowntimescancreatehundredsofthousandsofdollar,ifnotmulti-million,of loss inrevenue.Company’sstockcanplummetafteranoutage.Sometimes,refundsmustbegiventocustomersasaformofapology.Asrivalsalwaysseektocapitalizeanoutage,millionsofuserscanswitchtoanothercompetitor,acompany’sworstnightmare.Inthistalk,Iwillsharemanystudiesthatmyresearchgrouphasperformedinthelast8years.WeconductedacloudoutagestudyoftensofpopularInternetservicesbyanalyzingover1000headlinenewsandpublicpost-mortemreportsthatdetailhundredsofunplannedoutages.Wealsoconductedacomprehensivestudyofover3000bugreportsinmanypopularopen-sourcecloudsystems.Finally,wealsolookedintofail-slowhardware,anunder-studiedfailuremode,bycollectingover


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100anecdotalevidenceoffail-slowhardwareincidentsin12largeinstitutions.Wehopethesestudies canhelp cloud architects and engineers buildmore andmore reliable systems in thefuture.


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KEYNOTESPEAKERS

Prof.HadiSusanto

ProfessorattheDepartmentofMathematicsofKhalifaUniversity

Profile

HadiSusantowasbornandgrewupinLumajang(watchthebeautyoftheregionhere),EastJavaprovinceofIndonesia.Hewasanundergraduatestudent(BSc2001)intheDepartmentofMathematics of InstitutTeknologiBandung,with a thesisstudysupervisedbyBarberavandeFliertandEdySoewono.Hesubsequentlydidhispostgraduatestudies(MSc2003,PhD2006) in the Department of Applied Mathematics of theUniversity of Twente under the supervision of Stephan vanGils.HewasVisitingAssistantProfessor (2005-2007)at theDepartmentofMathematicsandStatisticsoftheUniversityofMassachusetts, Amherst mentored by Panos Kevrekidis. HewasthenLecturerinAppliedMathematics(2008-2013)atthe

School of Mathematical Sciences of the University of Nottingham. In 2013, he moved to theDepartment ofMathematical Sciences of theUniversity of Essex as Senior Lecturer and thenProfessorofAppliedMathematics.Heiscurrentlyon-leavefromEssexandisProfessorattheDepartmentofMathematicsofKhalifaUniversity.HeisalsoAdjunctProfessor(GuruBesarLuarBiasa)intheDepartmentofMathematics,InstitutTeknologiBandung.Hisresearchinterestsareintheoreticalandcomputationaldynamicalsystemsandanalysisappliedtothestudyof,amongothers,nonlinearwavesindifferentialequationsthatmodelphysicalreality inmanydifferentfields,includingbiology,condensedmatterphysicsandnonlinearoptics.Healsoenjoyswritingliterature,includingpoetry,proseandessay.Title:COVID-19modellinginIndonesia:Amathematician’sapologyAbstractTheemergenceofSARS-CoV-2inDecember2019inChinaanditsworldwidedisseminationhasbecome amajor public health priority, including in Indonesia. In this talk, I will share somebackgroundstoriesofmyinvolvementintheSIMCOVIDconsortiumthatmodelthediseaseandhelplocalgovernmentsinthecountryinfightingthepandemic.Iwilldiscusssomemathematicalmodels and methods that we developed, that have been used to extract information fordevelopingandevaluatingpolicyresponses.Iwillalsodiscussmy‘apologies’onthelimitationsofthework.


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KEYNOTESPEAKERS

RezaKhorshidi,D.Phil.

ProgrammeLead,MachineLearningandBiomedicalInformaticsatTheUniversityof

Oxford

Profile

Dr.RezaKhorshidiiscurrentlythechiefscientistatAIG,andaprincipal investigator(inmachine learningandmedicine)atDeep Medicine program of The University of Oxford. HiscurrentresearchatOxfordisfocusedonprobabilisticmachinelearning, and deep sequence models, for biomedicalinformatics,populationhealth,andprecisionmedicine;morespecifically,heisinterestedinusingmachinelearningforthedevelopment of personalised health predictions andrecommendations, and an improved understanding ofmultimorbidity.Reza’steamatAIG(i.e., InvestmentsAI) isagroup of scientists, engineers, designers and productmanagers/strategists,primarily focusedonthedevelopmentofAI-firstproductsintheFinTechspace.

Title:FromModel.fit() toMarket.fit():Apath towards turningML research intoMLproductswithreal-worldfitAbstractScientistsinthefieldofmachinelearning(ML)–includingdeeplearning(DL)--aspiretobuildbettermodels(usually judgedbybeatingSOTAinwell-definedtasksanddatasets);successfulapplications of such models, on the other hand, are about product-market fit (PMF) inenvironmentswithever-growingcomplexities.AsmanyexpectMLtoplayabiggerroleinoursociety,MLscientists’abilitytoinfluencethisjourneywilldependonputtingMLresearchinaPMFcontextandviceversa(i.e.,optimizingformarket.fit()+⍺*model.fit(),insteadofoptimizingformodel.fit()alone).Therefore,inthistalk,IaimtocoverthegeneralprincipalsofbuildingAIproductsinthe“realworld”,coveringtopicssuchasAIproduct-marketfitandimpactevaluationinmedicine


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KEYNOTESPEAKERS

SolehAyubi,Ph.D

ChiefDigitalHealthcareOfficeratBioFarmaIndonesia

Profile

SolehAyubicurrentlytrustedasDirectorofTransformation&DigitalofPTBioFarma(Persero)aswellasChairmanofBUMNMuda.PriortohiscareerinSTATE-OWNEDenterprises,Ayubiwas a business, tech, & healthcare professional, who had avariety of prestigious experiences in various internationalhealthcompaniesintheUnitedStates.SolehgraduatedfromtheInformaticsEngineeringProgramin2005,thengraduatedfromtheInformaticsMasterProgramin2007,bothfromtheBandung Institute of Technology. Soleh continued hiseducationandobtainedaDoctorofPhilosophy(Ph.D)degreeinHealthSciencesfromtheUniversityofPittsburghin2012.Before returning to Indonesia and joining Bio Farma, Soleh

had 12 years of global experience in healthcare by joining several international companies;amongothers,NovoNordisk(Seattle,UnitedStates)asDirector,HeadofDigitalTherapeuticsandData Science for 2019-2020; Unitedhealth Group (Boston, United States) as Director ofInnovation, Research and Development in 2017 - 2019; Boston Children's Hospital (Boston,United States) as Technical Innovation Manager 2013-2016; Veterans Affairs Hospital(Pittsburgh, USA) as Senior Software Engineer in 2011-2012. Besides in theworld of healthindustry,SolehAlAyubiisalsoactiveintheworldofeducationandwasappointedtobecomeDigitalandHealthEntrepreneurshipatHarvardUniversity(Boston,UnitedStates)since2017untilnow,whichhasthetaskofprovidingtrainingandmentoringtomedicalstudents,doctoral(PhD)andpost-doctoralstudentsatHarvardMedicalSchool.Since2020,hehasalsobecometheAdjunctFacultyattheSchoolofBusinessManagement,BandungInstituteofTechnology.Inthescientific andR&Dcommunities, besidesholding2patents inAmerica, Soleh is also active invarious international conferences and publications in the fields of healthcare innovation andhealthcaretechnologybothasaspeaker,reviewer,andasajournalwriter.Asawriter,Solehhaspublished15papersininternationaljournalsandproceedings.Title:Covid-19VaccineDistribution:anexampleofAcceleratinghealthcaretransformationAbstractIndonesia is severely impactedbyCOVID-19withmore than twomillion cases anda currentthousanddeathsaday.Witha270millionpopulationlivingin514citieswithin6,000islands,itisalogisticalnightmareforvaccinedistribution.TonavigatethatchallengeBioFarmaasastate-ownedenterprisespecializedinvaccineproductionanddistributionhasbeencollaboratingwithseveraltechnologycompaniescoordinatedbyTelkomtodevelopaSupplyChain4.0systemofCOVID-19vaccinedistribution.


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Ablessingindisguise,the3-yearstalleddevelopmentoftheSupplyChain4.0calledTrackandTracesystem,wascatalyzedbythispandemic.Thesystemenablesustotrackandtraceeveryvialofvaccinefromthefirstmileofproductionpipelinetothelastmiledeliveryattheclinicorhospital.Thesystemappliesa2-DDataMatrixinvaccinevial,secondarypackage,andtertiarypackagewhich allows overall product identification, traceability, and authentication from end-to-end.UtilizingIoTsensors,thesystemsteadilyrecordsthelocationandtemperatureofvaccineswhilebeingstoredortransportedtoensurevaccinequalityuntilthelastmile.Thesystemisintegrablewithothermonitoringsuchasseniorleader’sdashboardattheMinistryofHealth,theMinistryof State-ownedEnterprises, andother senior leaderoffices.This allows forwell-orchestratedoperations,fastresponsetothechallengeinoperationsanddata-drivendecisionmakingforbothoperationsandstrategies.


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KEYNOTESPEAKERS

Dr.MohamadHardymanBarawi

UniversitiMalaysiaSarawak

ProfileHardyman is currently an academician at the UNIMAS. HereceivedhisPhDinComputingSciencefromtheUniversityofAberdeen. Before joining UNIMAS, he was an OperationSupportSpecialistatHewlettPackardandaSystemEngineeratMiszaTechnology.He is best known for his work on sentiment analysis andopinion mining, automatically labelling sentiment-bearingtopics, abstractive and extractive labelling, and opinionsummarization.Overthelastfewyears,hehasbeeninterestedinstatistical techniques fornatural languagegeneration.Hisresearchinthisareahasincludedworkinthesubareasoftext

summarization, topic modelling, and, more recently, opinion summarization through wordstatistics,linearapproach,andlexicalresourceacquisitionthroughstatisticalmeans.Title:Modelling Sentiment and Contrastive Opinion of COVID-19 Pandemic on Social Media:LinkingComputerScienceandSocialScienceAbstractSentiment analysis is the computational study of people’s opinions, attitudes, and emotionsexpressedinatextorwrittenlanguage.Duetomanychallengingresearchproblemsandawiderangeofpracticalapplications, ithasbecomeoneof themostactiveresearchareas innaturallanguageprocessing inrecentyears. In this talk, Iwilldiscussmainstreamsentimentanalysisresearch before moving on to describe some more recent work on modelling opinion andcommentsofCOVID-19PandemiconSocialMedia.Thisresearchnaturallyconnectscomputerscience and social science, especially communication and political sciences, in social mediaanalysis.


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KNOWLEDGETRANSFER

WuShiwei

HUAWEICLOUD,CTOofAPACRegion

Profile

15 years working in the IT industry, I have experiencedvariousroles fromdeveloperto leadsolutionarchitect,withvariousprojectsinvariousenvironmentsfromastartupofadozenpeopletoatop100MNC.AsCTOofHuaweiCloud,APACregion,oneofmykeyresponsibilities is to lead thesolutionteam to address technical challenges from variousprojects/clients with proper solution designs; advise R&Dteamregarding toproduct/serviceenhancementsaswell assupportbuildingpartnereco-systemofHuaweiCloudinthisregion.

Title:Workforce Enablement in the New Normal of Post-Pandemic, Powered by CloudTechnologyAbstractTheoutbreakofpandemicwasquiteunexpected.Manydigitalsolutions/innovationswereputinto place for emergency use only, without considering the long term impact, especially insupportingworkforce;however it isbecomingincreasingly likelythatwewillbe livinginthisnewnormalthusweneedtorethink,redesigntheinitialsolutionstoenhancesecurity,enrichfeaturesandimproveuserexperience.Inthissession,Iwillexploresometypicaldigitalsolutionsforworkforceenablementanddiscusshowthesetypesoftechnologymayimpactourworkandlifeandwhatfeaturesthatneedtobeenabledtobecomemoresustainable.


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TUTORIALSPEAKERS

Dr.BayuErfianto

SeniorLecturerandresearcheratSchoolofComputingTelkomUniversity

ProfileBayuErfiantograduatedfromtheDepartmentofMathematics,majoring in Computer Sciences, Universitas Padjadjaran(UNPAD), Bandung, Indonesia in 1999 for his Bachelor ofSciences(S.Si.).HecontinuedhisMasterDegreeinTelematics,UniversiteitTwente,theNetherlandsfrom2002to2004.HealsocontinuedhisMasterDegreeinInformationTechnology,UniversitiTeknologiPetronasMalaysiafrom2007to2009.Hereceived Doctoral Degree in Electrical Engineering fromSchool of Electrical Engineering and Informatics (STEI),Institut Teknologi Bandung in 2017. Dr. Bayu was also asPostdoctoral researcher in School of Electrical EngineeringandInformatics(STEI)in2017-2018.Bayuiswiththeschool

ofcomputing(FakultasInformatika),TelkomUniversity,Bandung,IndonesiaasLecturersince2002 (a.k.a. STT Telkom). His research interests are in the field of Cyber-physical System,including:InternetofThings,NetworkandControlSystemofCyber-physicalSystem,andFormalModellingofCyber-physicalSystem.Title:Chest-wallMotionTrackingusingInertialMeasurementUnits(IMUs)AbstractInertialMeasurementUnits(IMUs) ismostlyembedded inwearabledevices.Nowadays, IMUsmotion tracking systems are allowing for long-lasting tracking of user motion in a situatedenvironment.Insteadofbodymotiontracking,ontheotherhand,wearingIMUsonthechestwalloffers a few advantages, such as for cardiac activity parameters estimation and respirationparameterestimation.Forexample,currentresearchshowsthatinertialsensorsarelow-costandeasy-to-use breathing-monitoring systems. Breathing parameters from chest-wall inclinationsignalsareeasilymeasuredusingIMU.ThistutorialpresentsseveraltechniquesforIMUsbasedmotion tracking to reconstruct chestwallmotionwith respect to angular velocity and linearacceleration.Severaltechniquesfordataorsignalprocessingarchitecturesarealsodiscussed.Thistutorialalsotriestointroducetheapplicationsofchest-wornIMUsbasedmotiontrackingtoestimateheart-rate,blood-pressure,andrespirationrate.


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TUTORIALSPEAKERS

DhanySaputra,Ph.D

DataScientistatBrüel&KjærVibro

Profile

Dr.DhanySaputraisabioinformaticiananddatascientist.HecurrentlyworksatBrüelandKjærVibro.HefinishedhisPh.D.inBioinformaticsattheCenterforGenomicEpidemiology,TUDenmark,in2013.Hehasparticipatedinmanybioinformaticscases, from controlling outbreaks, paleontology excavationanalysis, stool transplant, marine biology, food safety, andcancer research. He has anM.Sc. in Computer Science fromUniversitiTeknologiPETRONASMalaysiain2008andaB.Sc.in Information System from Sepuluh Nopember Institute ofTechnologySurabayaIndonesiain2005.

Title:BioinformaticshasHelpedPrevent&ControlaPandemic.What'sNext?AbstractBioinformaticsandcloudtechnologieshavebeenbeneficialinmanycountries'decision-makingduring this pandemic. Sequence analysis algorithms, such as fast algorithms on sequencealignment,de-novoassembly,geneannotation,geneexpressionanalysis,geneprediction,andantibiotic resistance finding, have been crucial in solving an outbreak. Nowadays, DNAsequencinghasbecomearoutinetask,andpeoplecanevendoitintheirkitchen.ImprovementsinDNAsequencingtechniqueshaveencouragedthepopularityofshotgunmetagenomics.Thewhole metagenome shotgun enables finding the microbial diversity - or in medicalbioinformatics,thelistofpathogens-inonesample.Additionally,itisalsotheprimarykeytodiscover the cure for genetic diseases and cancer. Together with microbiome research,bioinformaticshasencouragedfecalmicrobiotatransplant(FMT)researchtocuremanydiseasessuchasCDI,bipolardisorder,autism,andcancer.


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TUTORIALSPEAKERS

Assoc.Prof.Dr.Md.ShohelSayeed

AssociateProfessorandresearcheratMultimediaUniversity(MMU)

Profile

Dr. Shohel has more than 25 years meritorious workingexperienceandheholdsachallengingcareerwhichcombinesresearch,versatileadministrationandexcellentteaching.Hiscore research interest is in the area ofBiometrics, big data,cloudcomputing, artificial intelligence, information security,image and signal processing, pattern recognition andclassification. He has published over 60 research papers ininternational peer-reviewed journals and internationalconferenceproceedingsasaresultofhis researchwork.Dr.Shohel has been a member of Multimedia University since2001 and now he serves as an Associate Professor and theChairpersonofCentreforIntelligentCloudComputing(CICC)

oftheFacultyofInformationScienceandTechnology.HereceivedhisdoctorofphilosophyfromMultimedia University in Engineering, specializing in hand signature verification, and holdsmastersofinformationtechnologydegreefromtheUniversityKebangsaanMalaysia,specializinginindustrialcomputing.HereceivedhisBachelorofSciencedegreeinAgriculturalsciencefromBangladeshAgriculturalUniversity.Title:BigData:Trends,Challenges&OpportunitiesAbstractEveryday,approximately2.5quintillionbytesofdataarecreated.Thesedatacomefromdigitalpictures,videos,poststosocialmediasites,intelligentsensors,purchasetransactionrecords,cellphoneGPSsignals,tonameafew.ThisisknownasBigData.ThereisnodoubtthatBigDataandespeciallywhatwedowithithasthepotentialtobecomeadrivingforceforinnovationandvaluecreation.Innovationsintechnologyandgreateraffordabilityofdigitaldeviceshavepresidedovertoday’sAgeofBigData,anumbrellatermfortheexplosioninthequantityanddiversityofhighfrequencydigitaldata.Thesedataholdthepotentialasyet largelyuntappedtoallowdecisionmakerstotrackdevelopmentprogress,improvesocialprotection,andunderstandwhereexistingpolicies and programmes require adjustment. Turning Big Data—call logs, mobile-bankingtransactions, online user-generated content such as blog posts and Tweets, online searches,satellite images, etc. into actionable information requires using computational techniques tounveil trendsandpatternswithinandbetweentheseextremely largesocioeconomicdatasets.Newinsightsgleanedfromsuchdataminingshouldcomplementofficialstatistics,surveydata,and information generated by EarlyWarning Systems, adding depth and nuances on humanbehavioursandexperiencesanddoingsoinrealtime,therebynarrowingbothinformationandtimegaps.Thepromiseofdata-drivendecision-making isnowbeing recognizedbroadly, andthereisgrowingenthusiasmforthenotionof``BigData.’’Thereiscurrentlyawidegapbetweenitspotentialanditsrealization.


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Heterogeneity, scale, timeliness, complexity, and privacy problems with Big Data impedeprogressatallphasesofthepipelinethatcancreatevaluefromdata.Alargeamountofdatatodayisnotnativelyinstructuredformat;forexample,tweetsandblogsareweaklystructuredpiecesof text, while images and video are structured for storage and display, but not for semanticcontentandsearch.Transformingsuchcontent intoastructured format for lateranalysis isamajor challenge.Thevalueofdata explodeswhen it canbe linkedwithotherdata, thusdataintegration isamajorcreatorofvalue.Sincemostdata isdirectlygenerated indigital formattoday,wehavetheopportunityandthechallengebothtoinfluencethecreationtofacilitatelaterlinkageandtoautomaticallylinkpreviouslycreateddata.Dataanalysis,organization,retrieval,andmodelling are other foundational challenges. Data analysis is a clear bottleneck inmanyapplications,bothduetolackofscalabilityoftheunderlyingalgorithmsandduetothecomplexityofthedatathatneedstobeanalyzed.Finally,presentationoftheresultsanditsinterpretationbynon-technical domain experts is crucial to extracting actionable knowledge. Themany novelchallengesandopportunitiesassociatedwithBigDatanecessitate rethinkingmanyaspectsofthesedatamanagementplatforms,whileretainingotherdesirableaspects.ItshouldbepointoutthattheappropriateinvestmentinBigDatawillleadtoanewwaveoffundamentaltechnologicaladvancesthatwillbeembodied inthenextgenerationsofBigDatamanagementandanalysisplatforms,products,andsystems.Thus,weshouldbelievethattheseresearchproblemsarenotonlytimely,butalsohavethepotentialtocreatehugeeconomicvalueintheworldeconomyforyears to come.However, they are also hard, requiring us to rethink data analysis systems infundamentalways.AmajorinvestmentinBigData,properlydirected,canresultnotonlyinmajorscientificadvances,butalso laythefoundationforthenextgenerationofadvances inscience,medicine,andbusiness.


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TUTORIALSPEAKERS

MohEdiWibowo,S.Kom.,M.Kom.,Ph.D.

SeniorLecturerandresearcheratGadjahMadaUniversity(UGM)

Profile

Moh EdiWibowo received the bachelor degree andmasterdegreeincomputersciencefromUniversitasGadjahMadain2004and2006respectively,andthePh.D.degreeincomputerscience fromQueenslandUniversity of Technology in 2014.His current focus area is human and object detection andidentification. He currently works as the vice head of thedoctoralprogramofcomputerscienceat theDepartmentofComputerScienceandElectronics,UniversitasGadjahMada.

Title:FaceAnalysisinPublicSpaceAbstractThistutorialdiscussesvariousmethodstodetectandtoidentifyfaces,faciallandmarks,andfacialattributesinimagesandvideoscollectedfrompublicspaces.Suchmethodsprovideinformationthatmight be useful for public spacemonitoring and access control in particular during thecurrent pandemic situation. Algorithms such as cascaded regression and probabilisticdiscriminantanalysiswillbediscussedandimplementedinthetutorial.


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TUTORIALSPEAKERS

JuneidiTsai

TechnicalSpecialistatMicrosoft

ProfileCRMSpecialistwithmorethan10yearsofprofessionalworkexperience encompassing various facets of application anddata analysis across many industries including automotive,education,retail,andfinancialserviceindustry.

Title:MicrosoftEducationTransformationFrameworkforHigherEducationAbstractHighereducationinstitutionsarecomplexandmultifacetedorganizationscomprisedofmultipledepartments thatmustwork together toexecutea successfulvision.TheMicrosoftEducationTransformationFrameworkenablesaholisticlookattheinstitutionbutprovidesyouwiththeabilitytodevelopyourdigitalstrategyindiscretephases,answeringthatallimportantquestion,“WhereshouldIstart?”The Microsoft Education Transformation Framework provides practical advice to help youdevelop a strategy for digital transformationwith a holistic, long-term view implemented indiscretephasesthatyoucanbegintodaytohelpdriveinnovativestudentengagement,transformoperations,andensureasecure,connectedcampustoempoweryoutoreimagineeducation.


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SOCIALEVENTS

BirdsofafeatherSessions

Host:Afiahayati,Ph.DUniversitasGadjahMada

Theme:AIandBioinformaticsforCOVID-19

Afiahayati,S.Kom.,M.Cs,Ph.DisaLecturerandResearcherattheDepartmentofComputerScienceandElectronics,FacultyofMathematicsandNaturalSciences,GadjahMadaUniversity.ShefinishedherDoctoralDegreeinDept.ofBiosciencesandInformatics/Faculty of Science and Technology, KeioUniversity, Japan. Her research interests includeBioinformatics,MachineLearningandArtificialIntelligence.

Host:Dr.MuhammadJohanAlibasaTheme:DigitalfootprintsduringCOVID-19pandemics

Muhammad Johan Alibasa is a lecturer at the School ofComputing, T elkom University, Indonesia. He received theSchool of Computer Science Research Students ExcellencePrize2019fromtheUniversityofSydney,Australia,wherehecompletedhisdoctoraldegree.HewasalsoarecipientoftheIndonesian Endowment Fund for Education (LPDP)scholarship fromMinistryofFinance,Republicof Indonesia.His research interests include human-computer interactionand artificial intelligence, specifically on the applications ofmachinelearningtoimprovewellbeing.PriortohisPhDstudy,heworkedasamobileapplicationdeveloperanddevelopedmobileappsintheareaofworkplacewellbeing.Hereceived

theM.Sc.andB.Sc.degreesinelectricalengineeringfromInstitutTeknologiBandung,Indonesia,in2014and2012.


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SOCIALEVENTSBirdsofafeatherSessions

Host:KemasRahmatShaleh,Ph.D

TelkomUniversityTheme:KnowledgeManagement,KnowledgeRepresentationandreasoning

KemasRahmatSalehWiharjaisafulltimelecturerinTelkomUniversity since 2006. He got his Ph.D from University ofAberdeen in2020andhis research interestsareknowledgerepresentation and reasoning, knowledge graph, and datascience.

Host:Dr.AgusHartoyoTelkomUniversity

Theme:ArtificialIntelligenceofThings(AIoT):TheintersectionofAIandIoT

AgusHartoyoreceivedaB.Sc.inInformaticsfromITTelkom(nowTelkomUniversity),Indonesia,in2008onhisthesisinAIdevelopmentforsolvingaprobleminNLP.Hecompletedhis M.Sc. in Computer Science from TU Kaiserslautern,Germany,in2015withhisresearchworkfocusingonsolvinga reachability problem in the safety and reliability ofembedded systems. In 2021 he obtained a Ph.D. inBioinformatics from Swinburne University of Technology,Australia,ona thesis in the inferenceandmodelingofbraindynamics.HisPh.D.dissertationwasexaminedbytwoexpertsincomputationalbiology,oneofwhomgradedthethesiswiththehighestgrade:“Excellent-SummacumLaude”.Rightafter

hisPh.D.,AgusHartoyo joined theSchoolofComputing,TelkomUniversity, as a lecturer andresearcher.Hisresearchinterestsincludeartificialintelligence,statisticalinference,safetyandreliabilityofembeddedsystems,bioinformatics,andcomputationallinguistics.


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SOCIALEVENTSBirdsofafeatherSessions

Host:Dr.MayaAriyantiTelkomUniversity

Theme:ELearning

Dr.MayaAriyanti SE.,MM;Permanent lecturerofMasterofManagement Study Program, Faculty of Economics andBusiness,TelkomUniversitysince2008.CompletedBachelorofManagement inMarketingManagement Concentration attheFacultyofEconomics,ParahyanganUniversity,Bandung,1996, then continued to Master of Management (MM)Concentration inMarketingat theFacultyofEconomicsandBusiness, Padjadjaran University in in 1999 and Doctor ofEconomicswithaconcentrationinMarketingManagementatPadjadjaranUniversity-Bandungin2009.Hisfieldofstudyupto now is Marketing Management, Services and DigitalMarketing.Hasseveralarticlespublishedinreputablejournals

forthefield,haspublishedseveralbookssuchasCreditManagement,IntroductiontoMonetaryTheory, andMarketing Plan. The author is also a consultant andwrites cases in the field ofmarketing management. Currently the author is also a lecturer for management courses,introductiontobusiness,marketingmanagement,digitalmarketing,digitalconsumerbehavior,digitalbusinessconsumerbehavior,anddigitalmarketingcommunications.


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TECHNICALPARALLELSESSIONSCHEDULEParallelSession#1:Wednesday,August4,2021,11:00-13:00UTC/GMT+7

Session 1A–ApplicationsforPost-PandemicRecovery

Venue RoomA:Yudhistira

SessionChair Dr.AgusSihabuddin,S.Si.,M.Kom

Time PaperTitle Presenter

11:00TheroleoftechnologycapabilitiesandinnovationcapabilitiesinachievingbusinessresilienceofMSMEsduringCovid-19:EmpiricalStudy

GrisnaAnggadwita

11:20Gaze-ControlledDigitalSignageforPublicHealthEducationduringCovid-19Pandemic SunuWibirama

11:40AnalysisoftheHouseofRisk(HOR)ModelforRiskMitigationoftheSupplyChainManagementProcess(CaseStudy:KPBSPangalenganBandung,Indonesia)

RatihHendayani

12:00EvaluationoftheSocialRestrictionanditsEffecttotheCOVID-19SpreadinIndonesia

InnaSyafarina

12:20ContributingClinicalAttributestoCOVID-19MortalityinJakarta:MachineLearningStudy

MuhamadErzaAminanto

Session 1B–ComputerVision

Venue RoomB:Bima

SessionChair TeeConnie(MMU)


11:00Multi-TargetRegressionUsingConvolutionalNeuralNetwork-RandomForestforEarlyEarthquakeWarningSystem

AdiWibowo

11:20 Vision-BasedEmployeeActivityClassification RizalPutra

11:40CompressiveSensingImageWatermarkingOrthogonalMatchingPursuit

IrmaSafitri

12:00TrafficSignRecognitionwithConvolutionalNeuralNetwork

ZhongBoNg

12:20DeepConvolutionalGenerativeAdversarialNetworkApplicationinBatikPatternGenerator

Moch.ChamdaniMustaqim


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Session 1C–DataScience

Venue RoomC:Arjuna

SessionChair Dr.YuliantSibaroni


11:00ConvolutionalNeuralNetworksforIndonesianAspect-BasedSentimentAnalysisTourismReview AhmadHidayatullah

11:20SentimentAnalysisonMarketplaceReviewusingHybridLexiconandSVMMethod

MuhammadMukhtar

11:40ForecastingNumberofCOVID-19CasesinIndonesiawithARIMAandARIMAXModels

BimoAji

12:00DisasterTweetClassificationBasedOnGeospatialDataUsingtheBERT-MLPMethod IqbalMaulana

12:20CyberbullyingDetectiononIndonesianTwitterusingDoc2VecandConvolutionalNeuralNetwork

ShindyLaxmi

Session 1D–E-LearningandHCI

Venue RoomD:Nakula

SessionChair Dr.KusumaAyuLaksitowening


11:00DigitalNudgeEvaluationonCOVID-19tracingApplication

DyahSukmaningsih

11:20DevelopingSuicideRiskIdeaIdentificationforTeenager(SERIINA)MobileAppsPrototypeusingExtendedRapidApplicationDevelopment

TeniaWahyuningrum

11:40DesigningAnEducationalGameEvaluationFrameworkBasedOnGameMechanic

FaisZharfanAzif;SatrioRukmono

12:00CultivatingRecyclingAwarenessinPreschoolersusingAnimatedInteractiveComic

SitiZulaihaAhmad

12:20AnalysisInfluenceofTheOrganizationalLearningEnvironmentFactorsToEncourageEmployeeMotivationUsingE-Learning

NanikQodarsih


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Session 1E–Healthcare,Bioinformatics,andBiomedicalApplications

Venue RoomE:Sadewa

SessionChair Dr.DodiQory


11:00DistributedPhylogeneticTreeProcessingonBiologySequencesUsingMapreduce RenaningSusilo

11:20LinearRegressionModeltoPredicttheSpreadofCOVID-19inTangerangCity

YusufSudiyono

11:40StrategicInformationSystemPlanningforIndonesiaNon-franchisePharmaciesBasedonJohnWardandFactorAnalysisMethod

TabahArwiyanto

12:00FlexibleMulti-LayerConduraFabricUltraWide-BandAntennaForTelemedicineApplication

YusnitaRahayu

12:20AnonymizingPrescriptionDataAgainstIndividualPrivacyBreachinHealthcareDatabase

DediGunawan


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TECHNICALPARALLELSESSIONSCHEDULEParallelSession#2:Wednesday,August4,2021,14:00-16:00UTC/GMT+7

Session 2A–Networking,IoT,andSecurity


SessionChair Dr.BayuErfianto


14:00SimulationOfJellyfishTopologyLinkFailureHandlingUsingFloyd-WarshallandJohnsonAlgorithminSoftwareDefinedNetworkArchitecture

MuhammadNugroho

14:20GameTheoreticalPowerControlinHeterogeneousNetwork AnggunIsnawati

14:40IoTDroneCameraforaPaddyCropHealthDetectorwithRGBComparison

AjiPutrada

15:00 AReviewonIoTwithBigDataAnalytics AbuFuadAhmad

15:20VehicleBlindSpotAreaDetectionUsingBluetoothLowEnergyandMultilateration

AjiPutrada


Venue RoomB:Bima

SessionChair LimKianMing


14:00FacialEmotionRecognitionusingTransferLearningofAlexnet

SarmelaRajaSekaran

14:20VisuallySimilarHandwrittenChineseCharacterRecognitionwithConvolutionalNeuralNetwork

WeiHanLiu

14:40PneumoniaClassificationusingGabor-ConvolutionalNeuralNetworksandImageEnhancement

MuhammadAlfarizy

15:00FingerprintEnhancementusingIterativeContextualFilteringforFingerprintMatching

BramaSatria

15:20HistogramofOrientedGradientRandomTemplateProtectionforFaceVerification

LucasChongWeiJie


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Venue RoomC:Arjuna

SessionChair Dr.ImeldaAtastina


14:00Aspect-BasedSentimentAnalysisinBeautyProductReviewsUsingTF-IDFandSVMAlgorithm NadiraArthamevia

14:20AspectTermExtractionUsingDeepLearning-BasedApproachonIndonesianRestaurantReviews

RachmansyahAdhiWidhianto

14:40 SpamDetectiononIndonesianBeautyProductReviewMuhammadAhsan

Athallah

15:00EmotionClassificationonIndonesianTwitterUsingConvolutionalNeuralNetwork(CNN) FirhanMaulanaRusli

15:20MappingComplexTouristDestinationPreferences:NetworkPerspectives

DianRamadhani


Venue RoomD:Nakula

SessionChair DanaSulistyoKusumo,Ph.D


14:00ExploringtheexistenceandvariationofGamePlayerTraitsamongUndergraduatestudentsinMalaysia

MageswaranSanmugam

14:20ImplementationofContinuousIntegrationandContinuousDelivery(CI/CD)onAutomaticPerformanceTesting

MohammadRizkyPratama

14:40UnderstandingGovernmentReorganizationImpactfromKnowledgeManagementPerspective:AStudyCase

YuliaSulistyaningsih

15:00EnterpriseResourcePlanningTeachinginPostPandemicusingGamification

KaushalJheengut;DineshaCauleechurn;

BibiCadersaib

15:20 RPA-basedBotsforManagingOnlineLearningMaterials SitiFatimahAbdulRazak


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Venue RoomE:Sadewa

SessionChair SatriaMandala,Ph.D


14:00Holick'sRuleImplementation:CalculationofProducedVitaminDfromSunlightBasedonUVIndex,SkinType,andAreaofSunlightExposureontheBody

JonathanSalomo

14:20T-COFFEEMultipleSequenceAligneronHadoopSparkCluster

ViebiyantyPrihatiningrum

14:40RelaxationOscillatorUsingClosed-loopDualComparatorforBiomedicalApplications

TheodoraValerie

15:00 WirelessProgrammablebodysensornetworksandSituatedHealthcare

AlbertoFaro


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TECHNICALPARALLELSESSIONSCHEDULEParallelSession#3:Thursday,August5,2021,08:15-10:15UTC/GMT+7



SessionChair Dr.VeraSuryani


08:15DetectionofSinusoidswithFrequencyDriftinWhiteGaussianNoise

BradleyComar

08:35ToneDetectionSystemDesignforTargetswithFrequencyDrift

BradleyComar

08:55AADC3:Active-ActiveDistributedControllerwith3-in-1AsynchronousHeartbeatSynchronizationMethodinSoftware-DefinedNetworks

MuhammadNugroho

09:154GLTECellularNetworkCoveragePlanningandSimulationonMandalayAreawithPropagationModelCost-Hatta

AhmadIdris

09:35USBFlashDrivesForensicAnalysistoDetectCrownJewelDataBreachinPT.XYZ(CoffeeShopRetail-CaseStudy)

DanielSeptianto


Venue RoomB:Bima

SessionChair Dr.EmaRachmawati


08:15EnhancedAlexNetwithSuper-ResolutionforLow-ResolutionFaceRecognition

JinChyuanTan

08:35AnEnd-to-EndOpticalCharacterRecognitionPipelineforIndonesianIdentityCard

AndreasChandra

08:55 AStudyofBatikStyleTransferusingNeuralNetwork AdityaIhsan

09:15TomatoPlantDiseaseIdentificationthroughLeafImageusingConvolutionalNeuralNetwork

AuliaYoren


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Venue RoomC:Arjuna

SessionChair DiditAditya,Ph.D


08:15Non-StationaryOrderofVectorAutoregressioninSignificantOceanWaveForecasting FikkaRaudiya

08:35FN-Net:ADeepConvolutionalNeuralNetworkforFakeNewsDetection

KianLongTan

08:55SentimentAnalysisofOjekOnlineUserSatisfactionBasedontheNaïveBayesandNetBrandReputationMethod

AlamRahmatulloh

09:15RawPaperMaterialStockForecastingwithLongShort-TermMemory

FebryoKurniawan

09:35MobileCustomerBehaviourPredictiveAnalysisforTargetingNetflixPotentialCustomer

SuryadiTanuwijaya


Venue RoomD:Nakula

SessionChair SitiZainabIbrahim


08:15MasterDataManagementMaturityModel(MD3M)Assessment:ACaseStudyinSecretariatofPresidentialAdvisoryCouncil

ChielsinKo

08:35CapturingInstitutionandLearnersReadinessofe-LearningImplementation:ACaseStudyofaUniversityinBandung,Indonesia

DawamDwiJatmikoSuwawi

08:55SatisfactionFactorsofIndonesianNationalCivilServantRecruitmentSystem

GalihAvianto

09:15ImplementationandAnalysisofReusabilityFrameworkDesignforEventUserInterfaceComponentinPhaser3 DanaSulistyoKusumo

09:35ThePreliminaryStudyonthePerceptionofEngineeringStudentsonBlendedLearning

MinChiLow


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Venue RoomE:Sadewa

SessionChair NgChongHan


08:15ImplantSegmentationinRadiographicImageryUsingMultiresolutionMTANNandWaveletDecomposition RanggaPerwiratama

08:35ImprovingMulti-ClassMotorImageryEEGSignalsClassificationUsingEnsembleLearningMethod

DeniNugroho

08:55ImplementationandExperimentalCharacterizationofDual-BandWearableReflectorComposedofAMCStructureforWirelessCommunication

IchsanNusobri


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TECHNICALPARALLELSESSIONSCHEDULEParallelSession#4:Thursday,August5,2021,10:15–12:15UTC/GMT+7



SessionChair Dr.RioGunturUtomo


10:15ModifiedPixelValueOrdering-basedPredictorforReversibleDataHidingonVideo

TohariAhmad

10:35SimulationandAnalysisofPartialTransmitSequenceonPalmDateLeafClippingforPAPRValueReduction

VincentVincent

10:55 DesignAutomationofSinglePhotonCountingMethodforQuantumRandomNumberGeneration

DedyPutranto

11:15OntheModificationsofaDigitalSignatureAlgorithmwithSecretSharing

MaretaWahyuArdyani

11:35ConnectedVehicleCommunicationConceptforFloodLevelWarningUsingLowCostMicrocontroller

SumendraYogarayan;MohdFikriAzli

Abdullah

11:55Randomness,Uniqueness,andSteadinessEvaluationofPhysicalUnclonableFunctions

RivaldoSembiring


Venue RoomB:Bima

SessionChair WikkyFawwaz,Ph.D


10:15ALow-CostHigh-AccuracyThermalCameraUsingOff-the-shelfHardwareDevices

Dinh-TienTran

10:35Sentinel1ClassificationforGarlicLandIdentificationusingSupportVectorMachine

MuhammadAgmalaro;ImasSitanggang

10:55 RecognitionofAcademicEmotionsinOnlineClasses TeeConnie

11:15ImageSteganographyCompressiveSensingOrthogonalMatchingPursuit

IrmaSafitri


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Venue RoomC:Arjuna

SessionChair FaizalMakhrus,S.Kom.,M.Sc.,Ph.D


10:15IndonesianHoaxIdentificationonTweetsUsingDoc2Vec TitiWidaretna

10:35ElectronicNoseDatasetforClassifyingRiceQualityusingNeuralNetwork

FerdyErlangga

10:55SVMParallelConceptTestwithSMODecompositiononCancerMicroarrayDataset

RahmatPrasojoe

11:15DetectingOnlineRecruitmentFraudUsingMachineLearning HriditaTabassum

11:35DataminingforrevealingrelationshipbetweenGooglecommunitymobilityandmacro-economicindicators

GunawanGunawan


Venue RoomD:Nakula

SessionChair LewSookLing


10:15SuitableKnowledgeManagementProcessImplementation:acasestudyofPTSinergiSentraDigital

YusufPratama

10:35CriticalSuccessFactorsforProjectTrackingSoftwareImplementation:ACaseStudyataBankingCompanyinIndonesia

HendroHadi

10:55 AssuranceCasePatternusingSACMNotation NungkiSelviandro

11:15SustainabilityAndAptnessOfGameElementsInAGamifiedLearningEnvironment

MageswaranSanmugam

11:35UserInterfaceModelforVisualizationofLearningMaterialsinComicStripFormUsingGoal-DirectedDesignMethod

DanangJunaedi


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Session 4E–Networking,IoT,andSecurity

Venue RoomE:Sadewa

SessionChair ChongSiewChin


10:15AccessibilityandResponseTimeAnalysisontheCOVID19WebsiteinIndonesia RyanWicaksono

10:35ModifiedBitParityTechniqueforErrorDetectionof8BitData

FakhiraZulfira

10:55IoTApplicationonAgriculturalAreaSurveillanceandRemote-controlledIrrigationSystems

RatnasariRohmah

11:15Present-80EncryptionAlgorithmImplementationonGPRSArduinoMega-2560CyberPhysicalTrackingSystem

RiniWisnuWardhani

11:35HuntingCyberThreatsintheEnterpriseUsingNetworkDefenseLog

ArdianOktadika


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TECHNICALPARALLELSESSIONSCHEDULEParallelSession#5:Thursday,August5,2021,13:15–15:15UTC/GMT+7



SessionChair OoiShihYin


13:15BuildinganIDCardRepositorywithProgressiveWebApplicationtoMitigateFraudbasedontheTwelve-FactorAppmethodology

KevinAkbarAdhiguna

13:35 XB-Pot:RevealingHoneypot-basedAttacker'sBehaviors RyandyDjap

13:55DesignofaSnort-basedIDSontheRaspberryPi3ModelB+ApplyingTaZmenSnifferProtocolandLogAlertIntegrityAssurancewithSHA-3

RiniWisnuWardhani

14:15LearningMethodofPerformance-orientedCongestionControl(PCC)forVideoStreamingAnalysis

SidikPrabowo

14:35ExperimentalInvestigationofWaveAbsorberMadeofRingResonator-BasedAMCStructure IchsanNusobri

Session 5B–DataScience

Venue RoomB:Bima

SessionChair Dr.DyahArumingTyas,S.Si


13:15 InformationCascadeMechanismandMeasurementofIndonesianFakeNews

AslaSonia

13:35FraudAccountsIdentificationModellingonMulti-PlatformE-Commerce

GrawasSugiharto

13:55ClassificationonParticipantsRenewalProcessinInsuranceCompany:CaseStudyPTXYZ

NoperidaDamanik;DeddyUtomo

14:15HybridSpace-TimeModelandMachineLearningforForecastingMultivariateSpatio-TemporalData HendriPrabowo

14:35ComparativeStudyofCovid-19TweetsSentimentClassificationMethods

UntariWisesty


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Venue RoomC:Arjuna

SessionChair MuhammadAlfianAmrizal,B.Eng.,MIS,Ph.D


13:15CountDataForecastingusingPoissonAutoregression

forCOVID-19CasePredictioninJakarta BahrulNasution

13:35OptimizationofCropsAllocationPlanninginCianjurInvolvingWaterCostConstraintsUsingGenetic

AlgorithmBambangWahyudi

13:55 FakeNewsDetectionwithHybridCNN-LSTM KianLongTan

14:15AspectBasedSentimentAnalysisWithCombination

FeatureExtractionLDAandWord2vecRizkaVioOctrianyInggitSudiro

14:35SentimentAnalysisonBeautyProductReviewsusing

LSTMMethodMuhammadRafii

Danendra

Session 5D–DataScience

Venue RoomD:Nakula

SessionChair ChongLeeYing


13:15IndonesianIDCardExtractorUsingOpticalCharacterRecognitionandNaturalLanguagePost-Processing

FirhanRusli

13:35AnalysisofRecordsManagementMaturityLevelforDataCollectionofNetworkAssetsinIndonesianTelecommunicationIndustry

RizkyEkaPutri

13:55DataAcquisitionGuideforForestFireRiskModellinginMalaysia

YeeJianChew

14:15 ImplementationofHiddenMarkovModel(HMM)toPredictFinancialMarketRegime

IrmaPalupi

14:35PredictionofGraduationwithNaïveBayesAlgorithmandPrincipalComponentAnalysis(PCA)onTimeSeriesData

WishnuHerlambang


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Session 5E–DataScience

Venue RoomE:Sadewa

SessionChair OngThianSong


13:15ComparativeAnalysisofSupportVectorMachine(SVM)andRandomForest(RF)ClassificationforCancerDetectionusingMicroarray

IrawansyahIrawansyah

13:35EvaluatingtheBPPTMedicalSpeechCorpusforAnASRMedicalRecordTranscriptionSystem

ElviraNurfadhilah

13:55ImplementationofSimulatedAnnealing-SupportVectorMachineonQSARStudyofIndenopyrazoleDerivativeasAnti-CancerAgent

IsmanKurniawan

14:15RansomwareDetectiononBitcoinTransactionsUsingArtificialNeuralNetworkMethods

HairilHairil

14:35EmotionalContextDetectiononConversationTextwithDeepLearningMethodUsingLongShort-TermMemoryandAttentionNetworks

AfridaHelen


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ABSTRACTSESSION1A:ApplicationsforPost-PandemicRecovery

TheroleoftechnologycapabilitiesandinnovationcapabilitiesinachievingbusinessresilienceofMSMEs

duringCovid-19:EmpiricalStudy

GrisnaAnggadwita,RatihHendayani,ErniMartini,MuhammadKamil

Thisstudyaimstoidentifytheroleoftechnologicalcapabilitiesandinnovationcapabilitiesinthebusiness resilience of Micro, Small and Medium Enterprises (MSMEs) during the Covid-19pandemic.Thisstudyalsoexaminesthemediatingroleofinnovationcapabilityontherelationshipbetweentechnologycapabilityandbusinessresilience.Thisstudyusesaquantitativemethodwithacausalityapproachtoexaminetherelationshipbetweenvariables.Asurveywasconductedof400MSME owners in Jakarta, Indonesia using a random sampling technique. Structural equationmodels are used to predict and estimate relationships. The results of this study indicate thattechnological capabilities and innovation capabilities have a positive and significant effect onbusinessresilienceinMSMEsduringtheCovid-19pandemic.Inaddition,innovationcapabilityhasalsobeenshowntoplayasignificantroleasamediatorintherelationshipbetweentechnologicalcapability andbusiness resilience.This studyemphasizes that the increasing importanceof theconceptofbusinessresilience inthe faceof theCovid-19pandemicthusencouragingMSMEstoimprovetheirtechnologicalcapabilitiesandinnovationcapabilities.Gaze-ControlledDigitalSignageforPublicHealthEducationduringCovid-19Pandemic

SunuWibirama,SuatmiMurnani,IrawanDharmaSukawati,RidiFerdiana

Formorethanadecade,digitalsignageshavebeenusedinhealthfacilitiesandpublicenvironmenttoprovidefunandinteractiveapproachofeducation.Unfortunately,interactingwithconventionaldigitalsignageduringCovid-19pandemicraisesaconcernonitshygiene.Thus,touchlessinteractionis preferable to avoid direct contact on the touch screen. Here we present a novel study oneffectiveness of gaze-based interaction in a digital signage for public education about Covid-19.Insteadoftouchingthescreen,theusersengagewiththecontentbygazingatadynamicbuttonthatmovesinhorizontalorverticaldirection.Experimentalresultsshowthatgazingatfasterdynamicbuttons (angular speed of 60.28 ◦/s) than its slower counterpart (angular speed of 30.14 ◦/s)requiresshortertimetocompleteathree-stepstask(T=84,Z=−1.977,p<0.05).OurstudyprovidesascientificproofofconceptfordevelopmentoftouchlessdigitalsignagethatcomplieswithtechnicalguidelinesoftheWorldHealthOrganizationoncleaninganddisinfectingsurfacesinnon-healthcaresettings.

AnalysisoftheHouseofRisk(HOR)ModelforRiskMitigationoftheSupplyChainManagementProcess(CaseStudy:KPBSPangalenganBandung,Indonesia)

RatihHendayani,EllysaRahmadina,GrisnaAnggadwita,RinaPasaribu

ThisstudyidentifiesriskmanagementinthesupplychainofdairycompaniesinBandung,WestJava,Indonesia.Usingasinglecasestudymethodology,semi-structuredinterviewswereconductedwithvariousdepartmentsofsupplychainagentsworkingindairycompanies.Datawerecollectedaboutthedairyprocessingcompanysupplychainnetwork,manager'sknowledgeofsupplychainriskmanagement,andtherisksinherentinthemilksupplychain.Ingeneral,companieshavean


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awarenessofsupplychainrisks.Byusing theHouseofRisk(HOR)model,whichcombines twomethods, namely Failure Mode and Effect Analysis (FMEA) and Quality Function Deployment(QFD), the risks identified in the supply chain have been assessed and prioritized. Severalrecommendationshavebeenmadetomitigatehighpriorityrisks.Thestudyfound15riskeventsinthecompany'ssupplychainprocess,11riskagentswheresevenofthembelongtothepriorityriskagentcategorywhichhasashareof79.55%ofthetotalriskexperiencedbythecompany.Todealwiththesepriorityriskagents,thecompanyfoundsevenriskmitigationactionstominimizetheimpactthatcouldharmthecompany.

EvaluationoftheSocialRestrictionanditsEffecttotheCOVID-19SpreadinIndonesia

InnaSyafarina,AyuShabrina,ArnidaLailatulLatifah,DiditAdytiaThegovernmentofIndonesiahasimplementedalarge-scalesocialrestrictionofvariouslevels,inthe local andnational region to control theCOVID-19 transmission over the country. Successfulsocial restrictions are believed as a powerful way in controlling COVID-19 spread. This paperevaluatedthesocialrestrictionthathasbeenimplementedinIndonesiaanditsconnectiontothespreadofCOVID-19.Thesocialrestrictionisquantifiedbythechangesinhumanmovement,whilethespreadofCOVID-19iscomputedbyagrowthrateanddoublingtimeoftheCOVID-19cases.Thispaper showed the social restriction reduced human mobility up to more than 50% when therestriction was started. It flattened the cases but it only lasted for approximately twomonths.Afterward,thegrowthrateandthedoublingtimeshowedanincreaseoftheCOVID-19casesanditwas getting much worse starting at the end of the year 2020. Moreover, we found that theimplemented social restriction in Indonesia is less effective to reduce the COVID-19 spread inIndonesiaashumanmobilityduringaholiday,anespeciallylongweekendismuchstronger.

ContributingClinicalAttributestoCOVID-19MortalityinJakarta:MachineLearningStudy

MuhamadErzaErzaAminanto,BahrulIlmiNasution,AndiSulasikin,YudhistiraNugraha,Juan

Kanggrawan,AlexLukmantoSuhermanSince December 2019, we have lived in a pandemic era of severe acute respiratory syndromecoronavirus2(SARS-CoV-2).MedicalrecordsofCOVID-19patientshavebeenreportedandanalyzedworldwide. TheHealthAgency of Jakarta, Indonesia, collected clinical symptoms, demographics,travelhistory,andmortalityinformationfromMarch2020uptonow.DespitemassiveresearchonCOVID-19patients'data,thesignificantclinicalsymptomsthatleadtoCOVID-19mortalityinJakartahavenotbeenwelldescribed to thebestof theauthors'knowledge.Weextracted theCOVID-19recordsinJakartaandcomparedthembetweenpatientswhoweredischargedanddeceased.Thispaperexamineseachclinicalsymptom'simportancetomortalityusingmachinelearningtechniques,namelyweightedArtificialNeuralNetwork,DecisionTree,andRandomForest.WeobservedthatPneumonia, Shortness of Breath,Malaise, Hypertension, Fever, and Runny Nose are the top sixsignificant clinical symptoms that lead todeaths in Jakarta.We suggestmedical expertsbecomemorecautiouswiththesesymptoms.Also,inmedicalfacilities,thesesymptomscanbeusedaspre-screeningbeforeenteringthefacilities.


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ABSTRACTSESSION1B:ComputerVision

Multi-TargetRegressionUsingConvolutionalNeuralNetwork-RandomForestForEarly

EarthquakeWarningSystem

BenaldyYugaAdhaityar,DavidSahara,CecepPratama,AdiWibowo,LeniSophiaHeliani

Indonesiaoccupiesaveryactivetectoniczonebecausetheworld'sthreelargeplatesandnineothersmallerplatesmeeteachotherinIndonesianterritoryandformacomplexplatemeetingpath.EastJavaProvinceispartoftheSundanesearc,whichhasarelativelyhighlevelofseismicityandhasacomplexgeologicalsystemthatisaresultoftheIndo-Australianplate.Therefore,asystemthatcanprovideearthquakeearlywarning(EEW)isneededtoreducecasualties.Inthispaper,wedeterminethe location andmagnitude of the earthquake using the Convolutional Neural Network (CNN) asfeatureextractionandRandomForest(RF)formulti-targetregression.EarthquakesinEastJavain2009-2017areusedtotrainandvalidatetheproposedmodel.Basedontheexperiment,thelowesterrorobtainedfromtheCNN-RFmodelis18kmforlongitude,32.2kmforlongitude,and0.3490formagnitude.

Vision-BasedEmployeeActivityClassification

RizalKusumaPutra,EmaRachmawati,FebryantiSthevanieAnemployeeshouldbecompetentandexpertiseintheirrespectivefields.Anevaluationisneededtomaintainthequalityofemployee'sperformance,oneofwhichcanbedonebyobservingtheiractivityduringworkinghours.Thisresearchdiscussestheclassificationoftheemployee'sactivityindeskwork.Classificationofemployee'sactivityisinvestigatedusingResNetandtheCyclicalLearningRatemethodinanoveldataset,i.e.vision-basedemployeeactivity.Classificationisdonebylookingatthreetypesofemployeeactivities:talkingonthephone,usingaPC,andplayingsmartphone.Themostoptimalresultof this research is ResNet50 using CLRwith image input of 224x224x3,which has an accuracy of87.01%and12.99%errorratefortalkingonthephone,99.95%accuracyand0.05%errorrateforusingapc,81.67%accuracyand18.83%errorrateforplayingsmartphone,andhasadecreasinglossvalue. In addition, this research shows that cyclical learning rate significantly affects the modelperformance.

CompressiveSensingImageWatermarkingOrthogonalMatchingPursuit

IrmaSafitriEaseofaccesstodigitaldataontheinternetispronetorampantpiracy,especiallyofcopyrightandownership in themultimedia industry.Therefore,weneeda security system that canprotectandsecurethecopyrightofdataownership.Watermarkingisonesolutiontoovercomepiracythatoccurs.Watermarkingallowstheembeddedconfidentialinformationtobesolvedbyotherparties.Thisstudydiscussesimagewatermarkingusingthediscretewavelettransform(DWT)methodwhichisusedtodecomposethehostimageasanimagewherethewatermarkisembedded,thespreadspectrum(SS)methodusedintheinsertionprocessbyspreadingwatermarkbitsonthehostimageandcompressivesensing (CS)which is used to increase the efficiency of thewatermark technique to increase thecapacityandperceptibilityofthewatermarkingprocess.Thereconstructionprocesswascarriedoutusing orthogonal matching pursuit (OMP). The result is that the host image has a watermarkembedded in thebest sub-band.The test results showed theperformanceof thePSNRvaluewas53.3553dB,MSE0.30029,andBER0.20027.


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TrafficSignRecognitionwithConvolutionalNeuralNetwork

ZhongBoNg,KianMingLim,ChinPooLee

ThispaperpresentsanablationanalysisofMultilayerPerceptronandConvolutionalNeuralNetworksin traffic sign recognition. The ablation analysis studies the effects of different architectures ofMultilayerPerceptronandConvolutionalNeuralNetworks,batchnormalisationanddropout.Atotalof8 different models are reviewed and their performance is studied. A traffic sign dataset withapproximately 5000 images is collected. The experimental results demonstrate that ConvolutionalNeuralNetworksoutperformMultilayerPerceptron ingeneral.Leveragingdropout layerandbatchnormalisationiseffectiveinimprovingthestabilityofthemodelthusyieldingbetterresults.

DeepConvolutionalGenerativeAdversarialNetworkApplicationinBatikPatternGenerator

AgusEkoMinarno,Moch.ChamdaniMustaqim,YufisAzhar,WahyuAndhykaKusuma

Batikhasbeenallegedasoneoftheoldestculturalheritagesworldwide.Sincetheinitiation,batikhasbeenidentifiedwithvarioustypesandpatterns.Variouskindsofbatikmakingtechniqueshavelongbeenpopularizedbythewidercommunity.DCGANservesasanewideaforthemodernbatikworld.Such algorithm is capable of reproducing a novel image to produce batik patterns previouslyunnotified.Hence,thisstudyaimstoproposeaDCGANmodeltocraftanewtypeofbatikpattern.ByutilizingalargedatasetofbatikimagesandavarietyoftheproposedDCGANmodels,theproposedalgorithmhassucceededincraftinganovelanddiversesyntheticbatikpattern.


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ABSTRACTSESSION1C:DataScience

ConvolutionalNeuralNetworksforIndonesianAspect-BasedSentimentAnalysisTourism

Review

RoyanNayoan,AhmadFathanHidayatullah,DhomasHattaFudholi

Inrecentyears,electronicwordofmouth(e-WOM)hasbeenwidelyusedbypeoplearoundtheworld.Tripadvisorisane-WOMtravelwebsitewhichprovidesinformationaboutreviewsandopinionsontravel-relatedcontent.Tohelpusersgather information faster, aspect-basedsentimentanalysis isnecessary.Aspect-based sentiment analysis helpsusers to capture and extract important featuresfromthereviews.Therefore, thisstudyaimstobuildanaspect-basedsentimentanalysismodelofIndonesian tourism review by extracting aspect-category and their corresponding polarities fromuser reviews. To gain the bestmodel, we performed several experiments by using ConvolutionalNeuralNetworks(CNN).Moreover,wecomparedourCNNmodelwithCNN-LSTMandCNN-GRUtoidentify thesentimentandaspects fromthereviews.Wealsoperformednegationhandling inourfeatureextractionprocess to improveourCNNmodels.Basedonourexperiments,CNNcombinedwith both POS tag and negation handling outperformed the other models with the accuracy ofsentimentanalysisof0.9522andaspectcategoryof0.9551.

SentimentAnalysisonMarketplaceReviewusingHybridLexiconandSVMMethod

MuhammadMukhtar,WikkyFawwazAlMaki,AdeRomadhonyNowadays,especiallyduringtheCovid-19pandemictime,thereisenormousriseinonlinetransactions.Thereareseveralpopularmarketplacesthatprovidereviewfacilitytohelpcustomerschoosingtherightproductsorsellers.SentimentanalysisisastudytoclassifyareviewtexttosentimentclassesTables.Inthispaper,wepresentastudyofsentimentanalysisonmarketplacereviewtextusinghybridmethod: based on lexicon fromSentiwordnet 3.0 and SupportVectorMachine (SVM)method. Theexperimental results show that the hybrid method outperforms the lexicon approach and SVMapproach.

ForecastingNumberofCOVID-19CasesinIndonesiawithARIMAandARIMAXModels

BimoSatrioAji,Indwiarti,AniqAtiqi

DuringthepandemicCOVID-19,Indonesiahasasignificantnumberofpositivecasesamongcountriesin Asia. In early December 2020, the death rate in Indonesia had been reached more than 3%.Meanwhile, the daily number of positive is also continued to increase, it happens due to lack ofanticipationrulesmadebylocalauthoritiesandcentralgovernment.Thus,thepreventivestepsuchforecastingbecomesamajorissueintheareaofscienceandtechnology,tomakeallstakeholderswell-prepared against this pandemic. This paper provides the performance of The AutoregressiveIntegratedMovingAverage(ARIMA)toforecastseveralCOVID-19andalsoexaminesAutoRegressiveIntegratedMovingAveragewithexogenousvariables(ARIMAX)modelbyconsideringGoogleTrendsasanexternalvariable.WeconsideradailydatasetfromtheofficialwebsiteoftheJakarta'sCOVID-19andtheGoogleTrendsdatabasedoncertainqueriesasexternalvariablesonMarch1-November25, 2020. According toARIMA andARIMAXmodels,we haveARIMAXmodelwithGoogle TrendsimprovingARIMA'sperformancebyreducingtheMAPEby0.8%.


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DisasterTweetClassificationBasedOnGeospatialDataUsingtheBERT-MLPMethod

IqbalMaulanaandWarihMaharaniAsapopularsocialmediaintheworldandeveninIndonesia,Twitterhasavarietyofpopulartopicsmakingthesetopicstrending,includingthetopicofnaturaldisastersthathaveoccurredinIndonesia.TheDKIJakartaflooddisasterinearly2020madeabigsceneontrendingtwittertopics.Thisstudyaims to classify these tweets into "flooded" and "not flooded" predictions with the tweets andgeospatial features. The model proposed for classifying is BERT-MLP. Bidirectional Encoder fromTransformers (BERT) is used in the pre-trained model to classify these tweets and Multi LayerPerceptron(MLP)isusedtoclassifygeospatialfeatures.Thescenariodesignedforthemodelfocusesonthepreprocessingoftweetsasfollowswithoutstopwordremoval,withoutstemming,withboth,andwithoutboth.Onceclassified,thetweetwillbevisualizedintoatwo-dimensionalinteractivemap.Thebestscenarioresultshaveanaccuracyof82%inscenarioswithoutstemmingandwithstopwordremoval.Thisisduetothestemmingprocesseliminatessomeofthefeaturesintweetsaround6%.Thisstudyalsoshowstherelationshipbetweentheinfluenceofnegativecontexttweetsonthe"notflooded"classwithanorientationof65%ofthetotaldata.However,definingmanualstopwordscanaffectbecausestopwordremovalwillnotdeletewordsthatstillhavecontextrelatedfeaturestothetopic.

CyberbullyingDetectiononIndonesianTwitterusingDoc2VecandConvolutionalNeuralNetwork

ShindyTrimariaLaxmi,RitaRismala,HaniNurrahmi

Cyberbullyingistheactofthreateningorendangeringothersbypostingtextorimagesthathumiliateorharasspeoplethroughthe internetorothercommunicationdevices.Accordingtoasurvey fromPollingIndonesiaandAsosiasiPenyelenggaraJasaInternetIndonesia(APJII)aboutcyberbullying,49%of 5900 participants claimed they have been bullied. Therefore, this research conducted with theintention topreventcyberbullyingacts,especially in Indonesia.Wecollected thedata fromTwitterbasedonTwitter'sTrendingkeywordswhichcorrelatedtocyberbullyevents.Thenwecombineditwiththedatafrompreviousresearch.Weobtainedatotalof1425tweets,consistsof393datalabeledascyberbullyand1032datalabeledasnon-cyberbully.Thereupon,webuildtheDoc2Vecmodelforfeaturesextraction,andaclassifiermodelusingthebaselineclassificationmethod(SVMandRF)andCNNtodetectthecyberbullytexts.TheresultsshowsthattheclassifierusingCNNandDoc2vechasthehighestF1-score,65.08%.


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ABSTRACTSESSION1D:E-LearningandHCI

DigitalNudgeEvaluationonCOVID-19tracingApplication

DyahWahyuSukmaningsih

Nudgeisconsideredasaninterventiontochangeuserbehaviorandinfluencedecisionmaking.Mobileappshavebecomeapartofoureverydaylife.Inthispandemicera,governmentsusemobileapps'technologytocontrolthespreadofCOVID-19infection.ManygovernmentsimplementedCOVID-19tracingapps,andcitizensareforcedtousetheappstorecordtheircontacttracingandalertthemiftheyhavecontactwithaninfectedperson.Thekeytocontroltheincreasingspreadofthevirusalsodepends on citizens adhere to health protocol. Government experience difficulty in enforcing theprotocol without strict surveillance. Therefore, building awareness of risk is essential-this studyattempttodesignnudgeinterventionintoCOVID-19tracingapps.Fromtheevaluationoftheoriginalapps,fournudgeinterventionscanbeappliedintheapplication.DevelopingSuicideRiskIdeaIdentificationforTeenager(SERIINA)MobileAppsPrototype

usingExtendedRapidApplicationDevelopment

TeniaWahyuningrum

Suicidedeathisthenumber15causeofdeathintheworld.Suicidecasesgoundetectedbecausetheperpetrator showsno signs beforehand. Therefore, it is necessary to identify the risk of suicide inadolescentsearly,accessedquickly,maintainsuserprivacy,andunderstanduserneeds.UsingtheRiskFactors of Suicidal Ideation (RFSI) questionnaire, it hoped to detect early suicide incidents inadolescents.ThisresearchproposedaSuicideRiskIdeaIdentificationforTeenager(SERIINA),amobileapplicationdevelopedusingtheExtendedRapidApplicationDevelopment(ERAD)method.TheERADmethodcombinestheconceptofdesignsprintandRADinthesystemdevelopmentcycle.Basedontheresearchresults,SERIINAmobileappsdevelopment iscompletedin19days.Thefunctionaltestingusing Black-Box testing shows that the application works well and compatible in the five mainscenarios.Theresultsofinterfacedesigntestingusingheuristicevaluationindicatethattheapplicationhasbeenwelldesigned,withausabilityvalueof2.34orabout72-85%accordingtodesignrules.

DesigningAnEducationalGameEvaluationFrameworkBasedOnGameMechanic

SatrioARukmono,FaisZharfanAzif,MuhammadZuhriCaturCandra

Childrenineverydaylifeareincreasinglyusingeducationalgames.However,thequalityofeachofthemanyeducationalgamesavailablevaries.Someevaluationframeworksexist,butmostarepronetotheevaluator'ssubjectivity,whichcannotbecomparedobjectively.Thisstudyaimsto formulateaframeworkthatevaluatesthequalityofeducationalgamesobjectivelybasedonthegamemechanicsused.TheframeworkisbuiltuponBloom'staxonomyasthebasistoascertaintheacademicsideandMDA (Mechanics-Dynamics-Aesthetics) Framework todistinguish the game side.Then, it assesseseacheducationalmechanicbasedonastandardintheevaluationframeworktoobtainanaccurate,quantifiablescoreasameasure.Validationoftheframeworkinvolvesusingtheframeworktoevaluateexistingeducationalgamesandcomparingtheresultswithexpertreviews.Withthisframework,aneducationalgamequalitycanbemeasuredobjectivelyandquantitativelybasedonthetechnicalandfundamentalelementsthatexistineachgame.


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CultivatingRecyclingAwarenessinPreschoolersusingAnimatedInteractiveComic

SitiZulaihaAhmad,AriffinAbdulMutalib,NurulAinaKamarulzamanRecyclingawareness is a crucial social responsibility thatneeds tobe cultivated in the communityespecially to the young generation. Knowledge of recycling awareness is less exposed to children,especiallyinschoolsinceitisanactivitythatisnotappliedbyparentsathomebecausetheyarebusywith work. Therefore, this study introduces an interactive animated comic of recycling, as analternative learning application to facilitate children in preschool in learning and gaining newknowledge. I-Recycle consists of numerous storylines such asweekly activities, collecting recycleditemsathomewithfamily,cleaningtheparkwithfriendsandlearningactivitiesintheclassroom.Thisstudy integrates all components using three-phase development model, which consists of pre-development,development,andpost-developmentphases.Usabilitytestinghasbeenconductedwith20 children in individual setting to ensure that i-Recycle is usable and learnable, as those are theimportantelementstocultivateawarenessamongthechildren.Theresultrevealedthat79.75%oftheparticipantsagreedi-Recycleisapplicable,easytouseandeasytolearn.Infact,theuseracceptanceresults also proven that the application is useful, easy to use and acceptable (μ = 3.9). The resultdisclosedthati-Recycleisinterestingfortargetuserstogainnewknowledgeandlearnaboutrecyclingthroughstorytellingandanimatedcomic.However, it requires improvement in termsofaudioandinterfacedesigntoensuretheapplicationcouldsufficetheneedsofpreschoolers.

AnalysisInfluenceofTheOrganizationalLearningEnvironmentFactorsToEncourageEmployeeMotivationUsingE-Learning

NanikQodarsih,AchmadHidayanto,MuhammadRifkiShihab

Thesuccessfuluseofe-learningofcoursedependsonhowmuchgreatemployeeshaveengagementtoe-learning.Relatedtothiscase,organizationsthatrelyontechnologye-learningsoweneedtobuildmotivationofemployees.Wecanbuildemployeemotivationwhenusee-learning,onethingthatthewriterlookimportantisorganizationalsupportlearningenvironment.Thisstudywillanswerhowtheinfluence of the organizational learning environment to encourage employee motivation using e-learning.TheresearchwasconductedinIndonesiaJudicialTrainingCentre.Theresearchmethodologyusedinthisstudyisacasestudyresearch,researchsurveyandquantitativeresearch.Thenumberofrespondentswho tookpart in the surveyamounted to318 responses.Thequestionnairedatawasprocessedusing theCBSEMmethod.Dataprocessing isdoneusing theAMOS22application.Thisresearchhasbeenproven toprovideanewperspectiveon the fieldof technologyand informationsystems how the influence of organizational learning environment factors such as managementsupport,technologysupport,organizationalsupport,friendsupport,andjobsupportcanaffecttheSelfDeterminationTheoryandTechnologyAcceptanceModelvariablesine-learningacceptance.


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ABSTRACTSESSION1E:Healthcare,Bioinformatics,and

BiomedicalApplication

DistributedPhylogeneticTreeProcessingonBiologySequencesUsingMapreduce

RenaningSusilo,Setyorini,SitiAmatullahKarimah

MultipleSequenceAlignment(MSA)isanimportantprocessintheanalysisofbiologicalsequencesbymakingcomparisonsofseveralbiologicalsequences.InsomeMSAalgorithms(suchasCLUSTALW,for example), the formation of phylogenetic trees as a guideline in the alignment process has animportant role in determining the accuracy of the final alignment results. From the whole MSAprocess,theformationofphylogenetictreecomputationaltimeincreasedasthenumberofsequencesincreases.Computing thesimilarity score forall sequencepairs takesa lotof time, causinga longcomputationtimeproblem.Inaddition,thisresearchusestheneighborjoinmethodasatechniquetobuild a pyloghenetic tree This Research examines the potential efficiency of computationalphylogenetictreesinparallelandisdistributedtotheHadoopenvironmentusingMapReduce.TheresultsshowedthataphylogenetictreecanbegeneratedusingMapReducecomputationandresults19%timeefficiencyfrom2and3computationnode.

LinearRegressionModeltoPredicttheSpreadofCOVID-19inTangerangCity

YusufSudiyono,AgungTrisetyarso,HarjantoPrabowo,MeylianaMeyliana

TheoutbreakofacuterespiratorysyndromevirusdiseaseinChinaattheendof2019hascausedaglobalepidemicaswellashighmortalityratesinaffectedcountries.ThisresearchaimedatexaminingtheextentofthespreadofconfirmedCovid-19casesinTangerangCity.ThedatausedincludedthedataofconfirmedCovid-19patients.Suchdatawasintegratedwithgeospatialdatafoundin13sub-districtsinTangerangCity.ThepredictionofthespreadofconfirmedCovid-19caseswasmadebyusingLinearRegressionmodel.TheresultsoftheMAPEcalculationwithavaluebelow10%in13districtsresultedinaverygoodpredictivevalue.Thispredictionresultedinagraphandwasconnectedtoeachotherinathematicmapcoordinatepointsystem.TheresultsoftheCovid-19spreadpredictionweredividedintoseveraldistrictsandindicatedwithdifferentcolorvariations.Therefore,thedarkertheresultingcoloronthethematicmapvisualization,indicateanincreaseinCovid-19casesthathaveoccurred.StrategicInformationSystemPlanningforIndonesiaNon-franchisePharmaciesBasedonJohn

WardandFactorAnalysisMethod

TabahArwiyanto,AdianFatchurRochim,RRizalIsnanto

ThisstudyaimstoproduceastrategicplanningproposalfortheIS/ITofnon-franchisepharmaciestobeabletocompetewithfranchisedpharmaciesandtorealizethevision,mission,andobjectivesofthe pharmacy. This researchwas conducted using amixed-methodwith a sequential explanatorydesign.Observationandinterviewmethodsareusedtoformulatethevision,mission,andobjectivesofthepharmacy.ThedataobtainedisusedasabasisformakingobjectivefactorsintheformofLikertscale questions. The results of the questionnaire were analyzed for validity and reliability usingPearson'sProductMomentandCronbach'sAlphaandshowedtheresultsofther-countvalueofallthequestionswerepositiveandgreaterthanther-table(r-table>0.457)withavalueofα=0.980,whichmeans thequestion items in thequestionnairevalidand reliable.Thesequestionsare thendescribedintheformofadecompositiondiagramwiththeconsiderationofvaluechainanalysis.Theoutput of the decomposition diagram is used as data for analyzing the company's success factors


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(CSFs).QuantitativefactoranalysiswasalsocarriedoutwiththehelpofstatisticalapplicationsusingBartlett'sTestofSphericityandtheKeizer-Meyer-Olkin(KMO)test.Asaresult,thechi-squarevalueis279.591andtheMSAvalueis0.738,soitcanbeconcludedthatthedatahasmettherequirementsforfactoring.Basedonthefactoranalysis,7(seven)componentswerealsoextractedwhichwerethenused to compile the application portfolio in the McFarlan quadrant. The Customer RelationshipManager(CRM)applicationandthePointofSales(POS)applicationoccupyastrategicposition,whiletheInventoryModule,FinanceModule,SalesModuleoccupyahighpotentialposition.FlexibleMulti-LayerConduraFabricUltraWide-BandAntennaForTelemedicineApplication

YusnitaRahayuandTasyaKirana

Inthemedicalworld,acheck-upisthefirststepdoctorstaketoobtaininformationaboutapatient'shealth.Many advances in telemedicine, includinghealth tracking, have advancedwith the times.Awearableantennawasdevelopedtoenhancethemonitoringprocessandmakeitmoreconvenientforpatients.Fabricsthatarebothflexibleandcomfortableusedareoftenapplied.Itisalsonecessarytohaveanantennawithsufficientdatatransmittingcapacity.AnUltraWide-Band(UWB)antennaisadevicethatcantransmitlargequantitiesofdata.ThisisbecausetheUWBantenna'sbandwidthhasawidetransmissionband.ThispaperproposesaUWBflexibletelemedicineantennaworkingat6.55GHz. The condura substrate is used with frog patch shaped. Condura fabric was proved to be aninteresting fabric for textile antennas because of its strength, constant thickness, and high waterresistancepropertiesFromsimulatedresults,theantennahas7.86GHzofbandwidthwith10.8dBiofgain.Thesimulatedreturnlossof-20.57dBisachievedat6.5GHz.

AnonymizingPrescriptionDataAgainstIndividualPrivacyBreachinHealthcareDatabase

DediGunawan,FatahAlIrsyadi,YusufSulistyoNugroho,MaryamM

Prescriptiondataisasubsetofthehealth-relateddatawhichcanbecollectedbydrugstoreduringthepatient'smedicationperiod.Ingeneral,prescriptiondataconsistsofasetoftransactionrecordswhichcontains patients name or patients identification number and their prescribed medicine name.Analyzingsuchdatausingdataminingtechniquesbringsvariousadvantagesfordrugstores.However,performingdataminingtaskisnottrivialforthedrugstoresandpossiblythedrugstoredispatchestheprescription data to another party for data analysis.While it can solve the data analysis problem,unfortunately, such activity may result in privacy breach since sensitive information i.e., types ofpatients'diseasedue to thedataminerhasbackgroundknowledge to infercertainmedicine to thediseasetype.Toguaranteeindividualprivacypreservingofthesensitiveinformationamethodcalleddataanonymizationshouldbeemployedpriortohandlingtheprescriptiondatatoanotherpartyfordata mining purpose. Current data anonymization technique such as suppression technique cansuccessfullyaddresstheproblem,however,itresultsinsignificantitemlostandconsequentlyalotofuseful information is lost from the database. Tominimize the side effect of the suppression basedtechnique, a data anonymization which is based on swapping techniques can be a solution.Experimental results show that the swappingmethod successfullyprotects individualprivacywithrespecttoreducethenumberofitemlost.


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ABSTRACTSESSION2A:Networking,IoT,andSecurity

SimulationOfJellyfishTopologyLinkFailureHandlingUsingFloyd-WarshallandJohnson

AlgorithminSoftwareDefinedNetworkArchitecture

MuhammadAriefNugrohoandAndrianRakhmatsyah

DatacenterdesignedtoaccommodateallITinfrastructure,whichiscurrentlyingreatdemandbythecompanies. In the data center, there are several types of network topologies, one ofwhich is theJellyfish Topology. The main problem in Jellyfish is link failure. Link failure causing connectivitybetweendevicesgoesdown. Inorder to solve thatproblem, it isnecessary todesignandsimulatebackup path for the networks. The emerging technology in networks called Software DefinedNetworks(SDN).UsingSDN,administratorcaneasilyprogramthenetworkviacontroller.SDNcansolvetheproblembyconfiguringthecontrollerinordertocreatebackuppathon-the-fly.Inordertofind the optimum backup path when link failure occurs in Jellyfish topology, floyd-warshall andjohnsonalgorithmareusedinthispaper.Simulationsarecarriedoutwithparametersconvergencetime, packet loss and throughput. Based on our experiments, FloydWarshall outperform Johnsonalgorithminallparameters.

GameTheoreticalPowerControlinHeterogeneousNetwork

AnggunFitrianIsnawatiandMasAlyAfandiThedevelopmentofwirelesstechnologyhaspenetratedinfemtocellcommunicationsystems,wherethis communication system is very flexible to develop. However, with many networks runningsimultaneously,which iscalledaheterogeneousnetwork,acombinationofmacrocelland femtocellnetworks,interferencebetweennetworksisunavoidable.Toresolvethisinterference,itcanbedonewithadaptivepowercontroltechniquesbytheuser.OneoftheadaptivepowercontrolmethodsisGameTheory.TheuseofGameTheoryonpowercontrolorGameTheoreticalPowerControlisoftenreferredtoPowerControlGame(PCG).Bydeterminingtheappropriateutilityfunction,theoptimalpowerisobtainedwhenusingthepowerupdateiterationprocess.TheresultsshowthatintheProposedmethodwhenitreachesaconvergentcondition,bothfemtousersandmacrousersareabletoreachSINRthatexceedsthetargetSINRof5.496forfemtousersand10.04formacrousers.Meanwhile,theDistributedPowerControl(DPC)methodisonlyabletoachievetheSINRuservaluewhichisthesameasthetargetSINR,whichis5and10forfemtousersandmacrousers,respectively.TheProposedmethodproducesahigherSINRvaluefortheuserthantheDPCmethodsothatintermsofachievingthetargetSINR,itcanbeconcludedthattheProposedmethodisbetterthantheDPC.

IoTDroneCameraforaPaddyCropHealthDetectorwithRGBComparison

ElvarettaYucky,AjiGautamaPutrada,MamanAbdurohmanThispaperproposesthesystemofpaddycrophealthdetectorusingdronecamera.Indonesiaisanagriculturalcountrythathasverylargeagriculturalland,whereeveryplanthealthmonitoringactivityisdonemanually.However, applying technologicaldevelopments in landmonitoringactivitieswillshortentimeandincreaseworkefficiency.Inthispaper,dronewitharaspberrypicamerahasusedtocaptureseveral imagesof rice fields fromseveral regions.The imagedatawillbeprocessed intoadigitalleafcolorchart(LCC)throughtheprocessofimageacquisition,RGBcolorextraction,andk-NNclassification.ThedatahasbeencomparedwiththerealLCC,whichisareferencetothehealthcolorofriceplants.Thepaddyfieldsthatareusedastheresearchmaterialare25daysafterplanting.The


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resultshowsthattheprecisionofthemethodis88.89%,therecallis93.02%,theaccuracyis98.22%,andthespecificityis98.77%.

AReviewonIoTwithBigDataAnalytics

AbuFuadAhmad,Md.ShohelSayeed,ChooPengTan,KimGeokTan,MdAhsanulBari,FerdousHossain

TheInternetofThings(IoT)isapowerfulandtransformativeforcefortheconvergenceofthephysicalanddigitalworldoftechnology.TheIoTisconnectingthings,businessesandpeopleinreal-timeandonamassive scale. The IoT is actually the network of interconnected devices that contains actuators,sensors,electronics,softwareandconnectivitywhichletsthesethingsconnect, interactandtransferdata.ConnecteddevicesandsoftwareworkinwaysthatproducemassiveamountsofdatawhereBigDatacomes intothepicture.Bigdata is thediversesetsof informationthatarebothvery largeandcomplexinnature.Bigdataoffersabetterwayofmanagingandusingalargeamountofdatawiththeopportunitytoconductdeeperandricheranalysis.AlthoughtheextensivenumberofworksdoneonbigdataanalyticsandIoT,theoverlappingofthesetwofieldsofstudycreatesvariouspossibilitiesforthrivingdataanalysis in the IoTenvironment.Thisarticleprovidesa thoroughreviewof therecentadvancementofIoTwithbigdataandanalytics.Wealsomakeareviewoftherelationshipbetweenthese fields. This article discusses the application area of IoT and big data analytics aswell as theopportunitiescreatedbyenablinganalyticsinanIoTsystem.

VehicleBlindSpotAreaDetectionUsingBluetoothLowEnergyandMultilateration

MuhammadRezaWidyaPratama,MamanAbdurohman,AjiGautamaPutradaBlindspotistheareaaroundthevehiclethatcannotbeseenbythedriver'sviewevenwiththehelpoftherearviewmirrorsonthevehicle.Thereforeasolutionisneededtoovercomethisproblem.Thisstudyproposesasystemthatcandetectotherroadusersintheblindspotarea.ThissystemusestheBluetoothLowEnergy(BLE)moduletodetectthepresenceofobjects(smartphones)belongingtootherroadusers.PredictionofthedistancetotheroaduserisobtainedbycalculatingtheRSSIvaluebetweentheuser'ssmartphoneandthedetectingBLEmodule.Meanwhile,theobject'spositionisobtainedbyusingamultilaterationalgorithm. Inthisstudy, theKalmanFilter isalsousedtosuppressthenoiseobtainedduringthetargetdetectionprocess.Detectionresultsintheformofuserdistanceandlocationpredictionaredisplayedonthedriver'ssmartphonescreen.Testingthedetectionofvehiclesinablindspotareainstationaryconditionsproducesanaccuracyvalueof88.3%.


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FacialEmotionRecognitionusingTransferLearningofAlexnet

SarmelaRajaSekaran,ChinPooLee,KianMingLim

ThispaperpresentsadeeplearningmethodbasedonAlexNetarchitectureforemotionrecognition.Theideabehindourproposedframeworkistransferlearning.Wemanipulatecertainlayersofthepre-trainedAlexNetmodelandfine-tunethemodelonthepubliclyavailablefacialexpressiondatasetssothatitcanperformemotionrecognition.Theproposedmodelistrainedandtestedontwowidelyusedfacialexpressiondatasets,namelyextendedCohn-Kanade(CK+)datasetandFERdataset.Theproposedframeworkoutperformstheexistingdeeplearningmethodsinfacialemotionrecognition.

VisuallySimilarHandwrittenChineseCharacterRecognitionwithConvolutionalNeuralNetwork

WeiHanLiu,KianMingLim,ChinPooLee

Computervisionhaspenetratedmanydomains, for instance, security, sports,healthandmedicine,agriculture, transportation, manufacturing, retail, and so like. One of the computer vision tasks ischaracter recognition. In this work, a visually similar handwritten Chinese character dataset iscollected.Subsequently,anenhancedconvolutionalneuralnetworkisproposedfortherecognitionofvisuallysimilarhandwrittenChinesecharacters.Theconvolutionalneuralnetworkisenhancedbythedropoutregularizationandearlystoppingmechanismtoreducetheoverfittingproblem.TheAdamoptimizerisalsoleveragedtoaccelerateandoptimizethetrainingprocessoftheconvolutionalneuralnetwork.Theempiricalresultsdemonstratethattheenhancedconvolutionalneuralnetworkachievesa97%accuracy,thuscorroborateithasbetterdiscriminatingpowerinvisuallysimilarhandwrittenChinesecharacterrecognition.

PneumoniaClassificationusingGabor-ConvolutionalNeuralNetworksandImageEnhancement

AgusEkoMinarnoandMuhammadAlfarizy

Pneumoniaisarespiratorydiseasecausedbybacterialand,viralorfungalinfectionsandhasahighmortalityrate.Pneumoniaisusuallycharacterizedbythepresenceoffluidintheairsacsofthelungsoralveoli.IdentificationofpneumoniacanbedonewithChestX-Rayimage,buthamperedbyotherlungproblems thathavebeenexperiencedby thepatient.Therefore, in this studyproposedDeepLearningwith CNNmethod to solve classification problems quickly and precisely. In solving thisproblem, this research proposed Gabor Filter-Convolutional Neural Network method and ImageEnhancementpre-processingtechnique.Beforetheimageisprocessed,dataaugmentationwillalsobecarriedoutbyseveral techniques.TheuseofGaborFiltermanaged togetgoodaccuracywhileImageEnhancementwaslesssuitableforuseinthiscase.However,combiningimageenhancementandGaborfiltergetssmallerloss.ThisstudyobtainedthehighestaccuracywiththeGaborFilter-CNNmodelof94.9%andgetlowestlosswiththeImageEnhancement-CNNmodel35.8%.


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FingerprintEnhancementusingIterativeContextualFilteringforFingerprintMatching

BramaYogaSatria,AgusBejo,RisanuriHidayatFingerprintmatchingdependsonthequalityofthefingerprintimages.Whenfingerprintimagequalityis low, itcandegradetheperformanceof fingerprintmatchingsignificantly.Fingerprint imagesareoftencontaminatedbynoise.Therefore,imagequalityiscrucialforfingerprintmatching.Inthispaper,an imageenhancementalgorithminwhichcontextual filtering isapplied iterativelytoa fingerprintimagehasbeenproposed.ThemainideaofthealgorithmistoiteratetheoutputoftheGaborfiltertogetbetterenhancementandmatchingperformance.Theresultofthealgorithmhasfivefilteredimagesduetofivetimesiteration.ItshowedthattheproposedmethodissignificantlybetterbasedonEqualErrorRate(EER)comparedtotheGaborfilterandthemodifiedGaborfilter.TheproposedmethodsurpassedtheGaborfilterby3.08%andthemodifiedGaborfilterby2.95%.

HistogramofOrientedGradientRandomTemplateProtectionforFaceVerification

LucasChongWeiJieandSiewChinChongPrivacypreservingschemeforfaceverificationisabiometricsystemwhichisembeddedwithtemplateprotectiontoprotectthedatainensuringdataintegrity.Inthispaper,anewmethoddubbedHistogramof Oriented Gradient Random Template Protection (HOGRTP) is proposed. The proposed methodutilizesHistogramofOrientedGradient approach as a feature extraction technique and fuseswithRandom Template Protectionmethod. The proposedmethod acts as amulti-factor authenticationtechniqueandaddsalayerofdataprotectiontoavoidthebiometriccompromisingissueduetothefactthatbiometricisirreplaceable.TheperformanceaccuracyofHOGRTPistestedontheunconstrainedface imagesusing thebenchmarkeddataset,LabeledFace in theWild (LFW).Apromisingresult isobtainedtoprovethatHOGRTPachieveshigherverificationrateinpercentagethanthepurebiometricscheme.


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Aspect-BasedSentimentAnalysisinBeautyProductReviewsUsingTF-IDFandSVMAlgorithm

NadiraPutriArthamevia,Adiwijaya,MahendraDwifebriPurbolaksono

Productreviewsareessentialine-commerceastheycanhelppotentialbuyersmakedecisionspriortomakingpurchasesandhelpsellersgetthemeasureoftheirproducts.Aproductcanhavethousandsofreviews,makingitburdensomeforpotentialbuyersandsellerstodrawaconclusionfromthoseabundantreviews.ThisresearchbuiltasystemthatappliesAspect-basedSentimentAnalysis(ABSA)withadatasetfromproductreviewsontheFemaleDailywebsite.ThesystemwasbuiltusingTF-IDFasitsfeatureextractionmethodcombinedwithwordbigramandwordbigram.TheSupportVectorMachine(SVM)algorithmisusedtoclassifythesentiments.Thisexperimentresultsindicatethatthepreprocessingstage,especiallythestemmingandstopwordsremovalprocessaregreatlyaffecttheaccuracyresults.ThechoiceofwordN-gramisalsocrucial,wherethisresearchshowsthatthewordunigramgivesahigheraccuracythanthewordbigram.ThefinalresultsofthisresearchshowthatTF-IDFcombinedwithwordunigramandSVMwithalinearkernelbringsoutthebestaccuracy,thatistosay,88.35%.

AspectTermExtractionUsingDeepLearning-BasedApproachonIndonesianRestaurantReview

RachmansyahAdhiWidhianto,AdeRomadhony

Aspect term extraction is a fundamental process in aspect-based sentiment analysis. Aspect termextractionaimstoidentifythereviewtextspanthatcontainstheaspectmentions.Inthispaper,wepresentourworkonaspecttermextractionforIndonesianrestaurantreviews,usingadeeplearning-basedapproach.WecollectedandannotatedanIndonesianrestaurantreviewsdataset,obtainedfroma restaurant reviewwebsite.Weperformed theannotationata token-levelandused the followingaspectlabelstoannotatethereviews:FOOD,PRICE,AMBIENCE,SERVICE,andMISCELLANEOUS.Thispaper treats aspect extraction as a token-level classification.We employed a ConvolutionalNeuralNetwork (CNN) model and Long Short-Term Memory (LSTM) model for the classification. TheexperimentalresultshowedthattheLSTMmethodgivesthebestperformance,withthemicroaverageF1-scoreis55,1%.

SpamDetectiononIndonesianBeautyProductReview

MuhammadAhsanAthallahandAdeRomadhony

Aproductreviewisoneofthemostimportantsourcesofinformationwhichcanhelpconsumerstofind themost suitable products for their needs. However, there is a chance a reviewer has otherintentionsthanprovidinganhonestreview,includingtoadvertisethebrandorotherbrands.Areviewthatdoesnotcontainanyinformationrelatedtotheproduct'saspects/featurescouldbeconsideredspam.Thispaperpresentsourworkonspamreviewdetection,specificallyinthedomainofbeautyproducts.We used SVM and Logistic Regression classifier and the following features: the reviewsentiments, product-related features, and review-centric features extracted from the reviews.Weclassifiedthebeautyproductreviewtextsasspamandnon-spamreviews.Theexperimentalresultshowed that the best accuracy percentagewas 81%, obtainedwhenwe used the sentiments andreview-centricfeatureswiththeSVMalgorithm.


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EmotionClassificationonIndonesianTwitterUsingConvolutionalNeuralNetwork(CNN)

FirhanMaulanaRusli,RitaRismala,HaniNurrahmiHumansareinseparablefromemotions,emotionsfillhumanlifeatalltimes.Emotionshaveanimpactonsocialrelationships,memoryanddecision-making.Intheeraofthisresearch,humanstendedtoexpressemotionsthroughsocialmediasuchasTwitterintheformofvideos,imagesandtext.Overtime,socialmediahasbecomeanimportantpartofmostpeople'slives.Emotiondetectionisaresearchareathatiswidelyresearched,especiallyinthefieldoflinguistics.Therefore,weclassifyemotionswithaConvolutionalNeuralNetwork.Thetweetdatausedwere4403tweetswhichwillbeclassifiedinto5classes, namely: love, joy, anger, sadness, and fear. In addition,we conduct feature engineering todecide thebest feature inemotion classification.The featuresused in this researchareWord2Vec,FastTextandGlove.F1-scoreisemployedasanevaluationmetric.TheresultsofourexperimentsshowthatbyimplementingthecombinationofCNNandWord2Vecourdatasetcanachieve72.06%ofF1-Score,whichincreasesthebaselinemodelby63.71%

MappingComplexTouristDestinationPreferences:NetworkPerspectives

DianPuteriRamadhani,AndryAlamsyah,MuhammadNashirAtmaja,JoePanjaitanGiventherapidevolutionofinformationtechnologyincreasesthenumberofindividualtouristswhochoosetoenjoyfreetravelwithoutdependingonguidebooksandtouragencyservices.Informationsharingactivitiescontinuewithout limitation facilitatesmost tourists toexploreand independentlydecide their destinations based on online traveller review pages. This freedom generates anincreasingly complex tourist visiting pattern. In other side, the abundance of data provides a newapproachinanalyzingthisvisitingpattern.Wecollected215,168reviewswrittenbytouristsallovertheworldregardingtouristdestinationsinBali,Indonesia.Thisresearchanalyzestheonlinetravellerreviewdatathroughassociationruleminingtechniquestodetectpairsoftouristvisitingdestinationandmappedthemthroughthenetworkanalysisapproach.Weseparateourexplorationbytracingthetourist visiting movement as the underlying factor to understand tourist visiting behaviour. Wediscovered themostpopular touristdestinationvisitingpatternand thedifferences in tourist visitpreferences for each continent. This research contributes to support efficient mobility in tourismmanagementbyprovidingtourismdestinationnetworksinsight.


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ExploringtheexistenceandvariationofGamePlayerTraitsamongUndergraduatestudentsin

Malaysia

MageswaranSanmugam

The use of game has become ordinary for students of generation-z whose live revolves aroundtechnology. The ease of access towards these games, via handheld devices such as tablets orsmartphones,cultivatesaspecifictypeofmotivationandaimsamongstudentswhenitcomestousinggames.Thesemotivationalgoalswill indirectlyinfluencewaysstudentsinvestigatetasksinvirtualformsthatmayormaynothaveasnippetofgame-likelearningenvironments.Assuch,itwillbevitalto identify thebreakdownofplayermotivationamongthesestudents.Participants froma tertiaryinstitutionweretestedusingBartlePlayerMotivationaftergoingthroughthelearningprocessviaane-learning. Itwas found that thehighest tendencywas forkiller typeplayersamong thestudents,followedbysocializers.Therewasadifferencewhen it came togenders; as such, the tendency toinvestigatetheneedsofindividualsplaysavitalrole.ImplementationofContinuousIntegrationandContinuousDelivery(CI/CD)onAutomatic

PerformanceTesting

DanaSulistyoKusumoandMohammadRizkyPratama

Performancetestisoneofthetestcomponentsinthedevelopmentphasetoensurethattheservicescreatedcanaccommodatethespecifiedtargetedperformanceloadandavoidperformancebottlenecks.Nowadays,withthedevelopmentofagiledevelopmentprocesses,thedevelopmentprocessescanrunfasteranditeratively.ContinuousIntegrationandContinuousDelivery(CI/CD)aremethodsusedinAgiledevelopmenttoautomateandspeedupthefollowingprocess:building,tests,andvalidationofservices.ThisresearchaimstoimplementCI/CDintheperformancetest.Intheexistingtest,theteststillneedshumanstoconductthetest.OurproposedsolutionforperformancetestswithCI/CDcanbeperformed automatically and reduce human role in the test. The implementation of CI/CD in theperformancetestmakestestprocessesintegrated,automatically,andperiodicallyexecuted.Itcanalsoquicklyrespondtoparametervaluechangesthatcanpreventthetesterfromsettingnewparametersinanewtestscenario.

UnderstandingGovernmentReorganizationImpactfromKnowledgeManagementPerspective:AStudyCase

YuliaSulistyaningsih,KhairiyahRizkiyah,SofianLusa,AssafArief

ReorganizationisacommonissuethatoftenoccursinIndonesia'sgovernmentinstitutionsthatcanincreasetheriskoflosingtacitknowledgeanddecreasetheagilityoftheinstitution'spublicservice.Properknowledgemanagement(KM)supportedbyadequateInformationTechnology(IT)canbeoneofthesolutions.However,thereissomeconsiderationrequiredtomakesuretheimplementationofKM can be done successfully. This study aims to take the initial step by evaluate the existingKMpracticeandassessKMimplementationreadinessusingaproposedmodelinoneofthegovernmentdepartmentsinXYZinstitution.Thisstudyusingquestionnairestocollectdatawhileinternalexpertsareinvolvedinvalidatingthequestionnaireandconfirmedtheassessmentresult.ThefindingshowsthatthereisstillnoKMstandardorpolicyappliesinthestudycase,andthereadinesshasreachedthelevelNotReadyNeedsSomeWorkwithascoreof2.909.Thesefindingscannotbeclaimedappliedto


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allgovernmentinstitutions,butitcanbethepilotassessmentfortheinstitution,andthehighlightedissuecanbelistedascommonfactorstobewareofinsimilarinstitutions'conditions.

EnterpriseResourcePlanningTeachinginPostPandemicusingGamification

SitiFatimahAbdulRazak,FaizunizaMashhod,ZulfadhliNajmibinZaidan

TheglobalCOVID-19pandemichasseenariseindigitallearningmaterialsbeingsharedwithstudentsofalllevelsofeducation.Learninginstitutionsusuallyprovidealearningmanagementsystemwhereall the notes, tutorial and example past year examination questions are provided for students tosupporttheir learningactivities incoursesthroughouttheirstudies.Studentsusuallydownloadthelearningmaterialseitheras.ziporindividualfilesinvariousfileformats.Thestepsarerepetitiveforeachregisteredcourse, thereforecanbetimeconsumingforstudents.Studentsalsoneedtohaveasenseofappropriatefilemanagementskillinordertoorganisedownloadedmaterialsforeasyaccesswhenever necessary. When the number of courses grow throughout the years, improper filesorganisationmayresultinlostofaccessorunidentifiablefilesinstudentmachineordevices.Hence,thepurposeofthispaperistoinvestigatethepotentialofRoboticProcessAutomation(RPA)toaddressrelatedchallengesfacedbystudentsinmanagingtheamountoflearningmaterialsprovidedthroughalearningmanagementsystemorportal.ARPA-basedbotwasdevelopedandintegratedwithalearningmanagementsystemtoaccomplishthegoals.TheintegrationshowsthatRPA-basedbotscanminimisestudentseffortinmanagingtheirlearningmaterialsefficiently.

RPA-basedBotsforManagingOnlineLearningMaterials

KaushalJheengut,DineshaCauleechurn,BibiZarineCadersaibThe past decade has been witnessing a considerable technological evolution, gearing companiestowardsaconstantcompetitivesense.OneofthewaystomaintainacompetitiveedgehasbeentheadoptionofEnterpriseResourcePlanning(ERP)systemstostreamlinebusinessprocesses,thereforesettingahighdemandforERPprofessionals.AlthoughuniversitieshaveERPintheircurricula,thereisstillaskillmismatchregardingERPskillswithindustry.ExistingteachingmethodsforERPconsistsofnumerous challenges that must be addressed to ensure learning and skill acquisition take place.Furthermore, the impact of the COVID-19 pandemic accelerated the populace to revert to onlinebehaviors,includingonlineteaching.ThispaperproposesagamingapproachtoERPteaching,withtheaimofcomplementingexistingtraditionalteachingmethods,ifnotreplacethem.Thegameexposestheusertobasicconceptsthroughonlinelecturesandthenengagestheuserindifferenttypesofgamessuch as crosswords, quizzes, scenario-based games and a simulation game. This game wasexperimentedwithagroupofuniversitystudentsinMauritiuswhoalreadyhadexposuretoanERPrelatedsubject.Overall,thegamewasmuchappreciatedbythestudentsandtheywerepositiveaboutitsimplementationintheteachingcurriculumthatisgraduallymetamorphosingintodistancelearning.


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Holick'sRuleImplementation:CalculationofProducedVitaminDfromSunlightBasedonUVIndex,SkinType,andAreaofSunlightExposureontheBody

JonathanSalomo,EduardusAriasena,AthayaSyaqra,SalmaMajidah

VitaminDisimportanttomaintaincardiovascular,skin,bone,andmentalhealth.Unfortunately,63%of Indonesianshave inadequate levelsofvitaminD.Oneof itsmajorcauses is the lackofpracticalmethodstomeasuretheamountofproducedvitaminDforeachindividual.Thisstudyconstructsapractical,mathematical formula tomeasure the amount of the produced vitaminD from sunlightexposure. It is conducted based onHolick's rule through skin type, body surface area exposed tosunlight, and ultraviolet (UV) index. Aided by correction fromUV spectrum analysis, the formulaenablespracticalmeasurementofvitaminDintakethroughsunlightforthegeneralpublic.Thefutureworksofthispapermayincludecorrectionsofmeasurementaccuracyandon-deviceimplementationofthealgorithm.

T-COFFEEMultipleSequenceAligneronHadoopSparkCluster

ViebiyantyPrihatiningrum,Setyorini,SitiAmatullahKarimah

DNA(DeoxyriboseNucleidAcid)isaseriesofnucleotideacidproteinsthatexistintheorganismbodywhere DNAwill be identicalwith inheritance. DNA in the organism body is in pairs, so biologicalanalysisisneededtomatchthesimilaritybetweentheDNAdata.SinceDNAcomputationusedhugeamountofdata,itshouldbecomputebyBigDatEnvironmenttocomputethesimilaritymatchingofdata.BigDataisusedforlarge-scalecomputationbyhavingseveralframeworksthatsupportsearchingforbiologysequencesimilarities.HadoopisaframeworkwhichisveryappropriateforrunningBigData.Inthisstudy,weusedMSA(MultipleSequenceAlignment)whereoneofthealgorithmswhichhas a high accuracy value is T-COFFEE (Tree Based Consistency Objective Function for AlignmentEvaluation) algorithm.T-COFFEE is an algorithm formultiple sequenceswhich is very suitable forfinding similarities in DNA data by focusing on very high accuracy values. Besides having a highaccuracyvalue,T-COFFEErequiresaverylongtimetoprocess.SothisresearchdidimplementationofT-COFFEEonhadoopparallelizationusingSparkwhichhasbeenproventoreducetheexecutiontime.


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RelaxationOscillatorUsingClosed-loopDualComparatorforBiomedicalApplications

TheodoraValerie,DodiGarinto,PraptoNugroho,ArySyahriar,HarkiYanto

People'srisingconcernsabouthealthhasfueledresearchintohigh-performancebiomedicaldevicesandcircuits,suchassensorsandimplantablesystemsonachip.Itisimportantforthesebiomedicaldevices tooperateat thesame lowfrequencyto treatcertainorgansanddiseases.Thus, theclockgenerator needs to be able to provide low frequency clock pulses with minimal size and highreliability.Theseconstraintselevatepowerconsumptionandproductioncostwithinthecircuitdesignto key parameters. One promising candidate is relaxation oscillator which have good on-chipcompatibility,andsuperiorfrequencystability.Thispaperproposesanovelrelaxationoscillatorusingclosed-loopdualcomparator.Afrequencyinthebandof50Hzto2.5kHzcanbegeneratedbyreplacingthevalueof resistorsandcapacitor inallpossiblecombinations.Thesimulationandexperimentalresultconfirmthatclosed-loopdualcomparator-basedrelaxationoscillatorprovidelowfrequencieswithlowercostandmoresimplicityduetolesscomponents.

WirelessProgrammablebodysensornetworksandSituatedHealthcare

AlbertoFaro,DanielaGiordano,MarioVenticinqueWirelessBodySensorNetworksrepresentan interestingchallengetoeffectivelycontroldangerouspathologyandtomonitorthewellbeingstatusofelderandfragilepeople.However,suchnetworksdon'ttakeintoaccountthecontextthathighlyinfluencethehealthstatus.Thiscounteractswiththenotionofsituatedhealthcaremoreandmoreclaimedfrombothsociologicalandmedicalpointofviewto provide effective healthcare interventions. Following this point of view, the paper illustrates aneffectivearchitecturetocontrolhealthstatusinthecontextofthebodilyactivityandtheenvironment.Inparticular,programmablewirelessbodysensornetworksrecentlyavailableonthemarket fusedwithenvironmentalportablesensorsareevaluatedtoprovideaviablesteptowardsasmartsituatedhealthcare,i.e.,monitoringandcontrolfromremotesitesandinrealtimepeopleintheircontextbyusingIoTdevicesprovidedwithartificialintelligence,i.e.,AIoTdevices,andsuitablewebservicesthatallowsuchsmartbodilynetworkstointernetworkwithnetworksofotherpeople,doctorsorhospitals.Currentlimitsandpotentialitiesarepointedoutbyacasestudy.


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DetectionofSinusoidswithFrequencyDriftinWhiteGaussianNoise

BradleyComar

In this study, 4 methods of detecting sinusoid targets in white Gaussian noise are investigated.AveragingFFTscoherentlyandnon-coherentlyisexamined.Abruteforcemethodisintroducedandinvestigated.UsingasinglelargeFFTspanningtheobservationwindowisalsoconsidered.Detectionperformanceisstudiedontargetswhosefrequenciesdriftovertime.Itisdeterminedthataveragingnon-coherentFFTsisasafechoiceinadetectionsystem.ThepreferredsizeoftheFFTusedfortonedetection aswell as the number of spectra to average is driven by the nature of the target tonefrequencydrift.This informationisespeciallyusefulwhenhighsampleratedetectorsareusedtofindtonesfromlowqualitytransmittersbecausemakingFFTstoolargeortakingtoomanyaveragesmayhinderdetection.

ToneDetectionSystemDesignforTargetswithFrequencyDrift

BradleyComarInthisstudy,3methodsoftonedetectionareinvestigated,thenon-coherentaveragingmethod,thepowerspectralmethod,andthecross-powerspectralmethod.Thetargettonesaresimulatedwithfrequencydrift.AnalysisisperformedtofindoptimalFFTsizesandnumberofspectratoaverage.PDvs.PFAcurvesare thencreatedandusedtocompare thesemethods.Thisanalysis isparticularlyusefulwhenusinghighsamplingratedetectorstofindtonesproducedwithlowerqualityoscillatorsthatexperiencefrequencydriftsinceusingFFTsthataretoolargeoraveragingtoomanyspectramayactuallydecreasedetectability.

AADC3:Active-ActiveDistributedControllerwith3-in-1AsynchronousHeartbeatSynchronizationMethodinSoftware-DefinedNetworks

MuhammadAriefNugrohoandVeraSuryani

TheproblemofDistributedControllers(active-active)usingsynchronousmessageexchangeisthateverymessagesentbycontrollerA(sender)willberespondedtobycontrollerB(receiver),thereforetheprocessofsendingthenextmessageafterreceivingthatresponse. It isCausingadecrease inperformance and an increase in the controller's workload because every message that will berespondedtorequiresanimmediateprocesstoproduceacknowledgments.Therefore,wedevelopedamethodtoimprovethemessageexchangemechanismandreducemessageresources'burdeninsending synchronization message information between controllers. This study proposes usingasynchronousmessagesasdistributedSDNcontrollermessageexchangeswiththe3-in-1method,which is the mechanism carried out by sending three messages and producing a reply in oneacknowledgment.Ourexperimentsshowthat3-in-1methodgivesloverCPUandmemoryusageandhasahigherthroughputthanthetraditionalmessageexchangeinDistributedSDNController.


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4GLTECellularNetworkCoveragePlanningandSimulationonMandalayAreawithPropagationModelCost-Hatt

AhmadIdrisandSuciRahmatia

Cellularnetworkisexpensive,beitthetechnologyorthesupportinginfrastructure.Inordertoachievemaximumresourceefficiencyandeffectiveness,aplanisneeded.Toestimatemostappropriatecellthroughput,todecreasethenumberofequipments,toanswertheneedfortrafficanalysis,andtogetthemost optimal capacity network planning and simulation are required. Network planning is aprocess that consists of several activitieswhose final target is todefine anoptimal cost- effectivenetworkdesign,whichisthenbuiltasamobilecellularnetwork.ThisresearchwasmadeinMandalayarea,locatedinMyanmarwiththetotalsizeofcomputationalzoneis656,198km²whileaimingtohaveabetterunderstandingonplanninganddoingsimulationonsaidregion.WithCost-Hattamodelpropagation, CVVPX310R1 antenna type, and E-UTRA Band 3 - 20MHz frequency band, 58 LTEtransmitterareplaced..

USBFlashDrivesForensicAnalysistoDetectCrownJewelDataBreachinPT.XYZ(CoffeeShop

Retail-CaseStudy)

DanielSeptiantoUSBflashdrivesisusedwidelyasportablestoragedevicesandbecomepopularchoicetostoredortransfer the data among the employee. There was a greater concern about leaks of informationespeciallycompanycrownjewelorintellectualpropertythroughUSBflashdrivesduetotheft,lost,negligence, or fraud. This research is a case study within PT. XYZ company which aims to findremaininginformationrelatedtothecompanycrownjewelorintellectualpropertyinsidetheUSBflashdriveswithincompanyenvironment.Theresearchshowedthatthesensitiveinformation(suchascompanycrownjewelorintellectualproperty,andcustomercreditcarddata)couldberecoveredfromtheUSBflashdrives.Itcouldobtainhighriskimpact(reputational)tothecompanythathaslowsecurityawareness.


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EnhancedAlexNetwithSuper-ResolutionforLow-ResolutionFaceRecognition

JinChyuanTan,ChinPooLee,KianMingLim

Withtheadvancement indeeplearning,high-resolutionfacerecognitionhasachievedoutstandingperformancethatmakesitwidelyadoptedinmanyreal-worldapplications.Facerecognitionplaysavitalroleinvisualsurveillancesystems.However,theimagescapturedbythesecuritycamerasareatlowresolutioncausingtheperformanceofthelow-resolutionfacerecognitionrelativelyinferior.Inviewofthis,weproposeanenhancedAlexNetwithSuper-ResolutionandDataAugmentation(SRDA-AlexNet)forlow-resolutionfacerecognition.Firstly,imagesuper-resolutionimprovesthequalityofthelow-resolutionimagestohigh-resolutionimages.Subsequently,dataaugmentationisappliedtogeneratevariationsoftheimagesforlargerdatasize.AnenhancedAlexNetwithbatchnormalizationanddropoutregularizationisthenusedforfeatureextraction.Thebatchnormalizationaimstoreducetheinternalcovariateshiftbynormalizingtheinputdistributionsofthemini-batches.Apartfromthat,thedropoutregularizationimprovesthegeneralizationcapabilityandalleviatestheoverfittingofthemodel. The extracted features are then classified using k-Nearest Neighbors method for low-resolutionfacerecognition.EmpiricalresultsdemonstratethattheproposedSRDA-AlexNetoutshinesthemethodsincomparison.

AnEnd-to-EndOpticalCharacterRecognitionPipelineforIndonesianIdentityCard

AndreasChandraandRubenStefanusOpticalCharacterRecognitionhasbeenlongstudiedoverthepastfewyears.Thechallengeremainsforthespecificpurposeofextractinginformationfromimagedocuments.Theaimofthisstudyistocreateanend-to-endpipelineforanIndonesianidentitycard.AdeeplearningapproachwasusedtolocalizeanareaoftextinterestbyusingFasterR-CNNwithResNet-50asthebackbone,YOLOv5forcharacterdetection,andcombinedmachinelearningalgorithmsusingtheRandomForestalgorithmtoclassifycharacters.Theproposedpipelineshowedaremarkableresultforbothidentitynumberandfullname.Thisprovidesapowerfultoolfortheauto-fillformandverificationprocesseffectivelyandefficiently.

AStudyofBatikStyleTransferusingNeuralNetwork

AdityaFirmanIhsanInthisstudy,tworemarkableapplicationsofconvolutionalneuralnetwork,i.e.texturesynthesisandstyletransferareappliedtobatiktexture.Individuallayersfromfourpre-trainednetworkssuchasVGG-19, Inception V3, ResNet-50, and DenseNet-121 are compared and analyzed. Different batikmotifswithsomespecificcriteriaarealsocomparedtoseethecapabilityoforiginalstyle transferalgorithm to regenerate concrete texture of Batik. Lastly, we propose a way to reconstruct batikimageswithsomepatternsfollowinganobject'sshapecontainedincontentimage.

TomatoPlantDiseaseIdentificationthroughLeafImageusingConvolutionalNeuralNetwork

AuliaIkvandaYorenandSuyantoSuyanto

Theproblemthatoftenoccursinagricultureisaboutdiseasesinplants.Plantdiseasescanresultinreducedyieldsfromagriculturalproduction.Therefore,thedetectionandanalysisofplantdiseasesare


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criticalandshouldbedoneasearlyaspossible.Diseasesinplantsoftenappearontheleaves,andthecharacteristicsof theaffected leavesareverydiverseanddifficult todistinguish.Thisphenomenonresultsindifficultyintheidentificationofplantdiseasesautomatically.Oneofthetechnologiesthatcanbeusedinidentifyingleafproblemsisdigitalimageprocessingtechnology.Theplantusedasacasestudyinthisresearchisthetomatoplant.AlternariaSolani,Septorialeafspot,Yellowvirusaresomeofthedisordersthattomatoplantscanexperience.Thesedisordersshouldbeclassifiedaccordingtotheirtype.Thisresearchdesignsasystemtoclassifythreetypesofdiseaseexperiencedbythetomatoplantleaves.Adatasetof4400 leaf images iscollectedand learnedto theConvolutionalNeuralNetwork(CNN)toclassifythreetomatoplantproblemsusingtheAugmentationprocess.Anevaluationusing5-foldcross-validationshowsthatCNNwithaugmentationdatagivesanaverageaccuracyof97.8%andthehighestaccuracyof99.5%.Thisresultisbetterthanthepreviousmethods:AlexNet,FasterR-CNN,andCNN+redgreenblue(RGB).


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Non-StationaryOrderofVectorAutoregressioninSignificantOceanWaveForecastin

FikkaRaudiyaandAniqAtiqi

Thispaperstudiestheimplementationofnonstationarymultivariatetimeseriesmodeltofitthewaveoceandata.Amodelwhichcomprisesaregressiontermandanassociationwithexogenousvariablesinaparticulartimehorizon.Becauseofthetrendfluctuationinthedataleadingtounstableprocess,differenceddataareusedinfittingthemodel.TheapproachsuggestedisappliedtothefiniteorderofVector Autoregression for an improved prediction simultaneously ofwave ocean by carrying outwind-related information to waves. The proposed model is compared with linear simpleAutoregressivemodel.Theperformanceofbothforecastingproceduresisassessedbymeansofwell-knownerrormeasures.The forecast basedon theproposedmethodology indicated that it canberegarded as a promisingmethod forwave ocean prediction, it outperforms using 4-order VectorAutoregression.

FN-Net:ADeepConvolutionalNeuralNetworkforFakeNewsDetection

KianLongTan,ChinPooLee,KianMingLim

Information and communication technology has evolved rapidly over the past decades, with asubstantialdevelopmentbeingtheemergenceofsocialmedia.It isthenewnormthatpeoplesharetheirinformationinstantlyandmassivelythroughthesocialmediaplatforms.Thedownsideofthisisthat the fake news also spread more rapidly and diffuse deeper than before. This has caused adevastating impact on people who aremisled by the fake news. In the interest of mitigating thisproblem,fakenewsdetectioniscrucialtohelppeopledifferentiatetheauthenticityofthenews.Inthisresearch,anenhancedconvolutionalneuralnetwork(CNN)model,referredtoasFakeNewsNet(FN-Net) isdevised for fakenewsdetection.TheFN-Netconsistsofmorepairsofconvolutionandmaxpooling layer to better encode the high-level features at different granularities. Besides that, tworegularization techniquesare incorporated into theFN-Net toaddress theoverfittingproblem.ThegradientdescentprocessofFN-NetisalsoacceleratedbytheAdamoptimizer.TheempiricalstudiesonfourdatasetsdemonstratesthatFN-NetoutshinestheoriginalCNNmodel.SentimentAnalysisofOjekOnlineUserSatisfactionBasedontheNaïveBayesandNetBrand

ReputationMethod

AlamRahmatulloh,RahmiShofa,IrfanDarmawan,ArdiansahArdiansah

GojekandGrabarethemostpopularonlinemotorcycletaxisandareoftenusedtodayinIndonesia,basedonHootsuite'ssurvey.However,itisnotyetknownhowtheresponsefromonlinemotorcycletaxiusers.SoitisnecessarytohaveasentimentanalysisofonlinemotorcycletaxiuserswhethertheyaresatisfiedordissatisfiedwiththedriversandGojekandGrabcompanies'services.Twitterwith52%activeusersofallinternetusersinIndonesiaallowsuserstowritevarioustopicssothattofindoutthelevelofusersatisfactionwithGojekandGrab.SentimentanalysiscanbeusedasareferenceforthedevelopmentofGojekandGrabservicesinthefuture.TheymeasurethelevelofsatisfactionwiththeNetBrandReputation(NBR)methodfromtheNaïveBayesclassificationresultsusingtherapidminertool.Theratingwithaccuracyhasanaccuracyvalueof99.80%forGojekand99.90%forGrab.ThisstudyshowsthatmoretweetshavenegativeopinionscomparedtopositiveopinionsforGojekandGrab.Namely616positiveopinionsand2317negativeopinionsforGojekdrivers,3560positiveopinionsand6419negativeopinionsforGojekCompany.594positiveopinions,and1866


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negativeopinionsforGrabdrivers.Aswellas3516positiveopinionsand4407negativeopinionsforGrabComp.

RawPaperMaterialStockForecastingwithLongShort-TermMemory

FebryoKurniawan,DyahHerwindiati,ManatapDolokLauro

The manufacturing business is one of the businesses in Indonesia that continues to show itsdevelopmentfromyeartoyear.Likeamanufacturingbusinessingeneral,oneoftheimportanteffortsmade in theprintingbusiness is the supplyof rawpapermaterials toproduce finishedgoods.Thepurposeofthisresearchismakingaforecastingoftherawpapermaterialforprintingcompanyon7differenttypesof269historicaldatawithweeklyintervalsfromJanuary2015toFebruary2020beforetheCovid19pandemicseason.ForecastingisdoneusingtheLongShortTermMemorymethodwithPythonlanguage.ThemodelarchitecturefortrainingandtestingiscarriedoutusingvanillaLSTMwithsingleinput,hiddenandoutputlayerwiththeconfigurationof64neuronsinthehiddenlayer,150epoch,12batchsizeandAdamOptimizer(lr=0.0001)whichwasrepeated10timesforbestresult.Thetestresultsshowthebestwindowsizelengthinthemodelforeachpaperrawmaterialdifferentlyfrom4to16.AllmodelswassuccessfullyforecastingthetestdatawithanaverageMAPEoftheoverallforecastof21.48%.

MobileCustomerBehaviourPredictiveAnalysisforTargetingNetflixPotentialCustomer

SuryadiTanuwijaya,AndryAlamsyah,MayaAriyantiThe development of Indonesia's ICT environment has made the mobile video-on-demand (VOD)platform one of the emerging lifestyles. With advanced smartphone technology, mobile phonesubscribers able to enjoy high-resolutionmobile VOD servicewith a greater user experience. ThepurposeofthisstudyistoprofileandpredictpotentialcustomersofoneoftheVODplatforms,Netflix,for personalizing marketing targets. Using machine learning predictive analytic methodology,customerprofileandbehaviordataaredividedinto3clustersusingtheK-Meansmodelbeforetestedwithseveralsupervisedmodelsforgettingthebestmodelforeachcluster.Featureimportanceanalysisisconductedtosupportmarketinginsightforproductofferingfollowsuptoeachtargetedpotentialcustomer.SignificantvariablesaffectingNetflixbuyersandnon-buyerswithin3differentclustersaredefined clearly with the number of potential customers that can be targeted as Netflix's futuresubscribers. The result shows themethod can be used by themobile operator to target potentialcustomerswitheffectivepromotionalorproductofferingbypersonalizedmarketingapproachbasedon the behavioral pattern and customer needs. It is expected by implementing this methodology,effectivityandaccuracyofmarketingeffortwillbeincreasedcomparedtotheconventionalmethod.


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MasterDataManagementMaturityModel(MD3M)Assessment:ACaseStudyinSecretariatof

PresidentialAdvisoryCouncil

ChielsinKo,AndytiasAdywiratama,AchmadHidayanto

ImplementationofMasterDataManagement(MDM)inanorganizationaimstohelptheprocessofconsolidatingandintegratingvarioussourcesofmasterdataintoasolitarytruthsource.MDMisalsoable to help to solve the complexity of data that occurs in the synchronization, consolidation andcleaningprocessofdatafromredundancy,andtoincreasethevalueofoperationalefficiencythroughthestandardizedcontentwhichrepresentsbusiness-basedprocess.Thegoalofthisstudyistoassessthe level of MDM maturity at Secretariat of Presidential Advisory Council during the COVID-19pandemicera.TheassessmentwasdoneusingMasterDataManagementMaturityModel(MD3M)bySpruit-Pietzka.ThemasterdatamanagementmaturitylevelintheorganizationwasassessedusingaquestionnairefilledoutbySMEsinagroupdiscussion.TheresultoftheassessmentshowedthatMDMmaturitylevelatSecretariatofPresidentialAdvisoryCouncilis1.Thereare61.29%(38oftotal62)implementedcapabilities.Thismeansthatorganizationalreadyhasawarenessinthemanagementofmasterdata.TheorganizationcanimproveitsMDMmaturitytoahigherlevelbyimplementingthemissingcapabilities.CapturingInstitutionandLearnersReadinessofe-LearningImplementation:ACaseStudyofa

UniversityinBandung,Indonesia

DawamDwiJatmikoSuwawi,BayuAditya,NungkiSelviandro,AnisaHerdiani,YatiRohayati,YanuarFirdausArieWibowo

E-learningreadinessassessmentisacriticalprocessthataninstitutionneedstodoinimplementinge-learning.Byconductingane-learningreadinessassessment,aninstitutioncouldidentifythefactorsthathinderthesuccessfulimplementationofe-learninganddevelopastrategicplantoenhancethee-learning implementation continuously. The higher education institution (HEI) needs to adopt thisapproach to implement e-learning successfully. Previous studies, such as inTurkeyHEIs, EgyptianUniversity,andtheUniversityofMysore,India,reportedthate-learningreadinessassessmentprovidesbenefits in defining a future direction in e-learning innovation. Telkom University, one of the topprivateuniversities inBandung, Indonesia, has launchedmanypolicies andprograms related to e-learning to provide high-level academic services and increase productivity and efficiency in thelearningprocess.Someindividualshaveraisedsomeresistancetothesepoliciesandprograms,buttheactualdataregardingthisisminimal.Themanagementneedsdatarelatedtoe-learningreadinesstointervene andmake this e-learning program a success. This study aims to capture the e-learningreadiness inTelkomUniversity fromthe institutionaland learner'sperspective.Data for thisstudywerecollectedusinganonlinequestionnaireaswellasdepth-interviewasarchivalsources.Besidesidentifying the e-learning readiness index, the output from this study is also to providerecommendationstopolicymakersregardingtheimplementationandthedevelopmentofe-learninginTelkomUniversity.

SatisfactionFactorsofIndonesianNationalCivilServantRecruitmentSystem

GalihKenangAvianto,FitriaElliyana,DanaI.Sensuse

Citizensatisfactionisthekeytothesuccessofe-governmentservices.Thecomputer-basedselectionsystem is one of the Indonesian government's services for recruiting civil servant candidates.


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Therefore,findingfactorsofIndonesianelectronicsatisfactionisgoaloftheresearch.Basedontheliteraturestudy,sixhypothesesandsixfactorswereidentifiedthatcandeterminethelevelofusersatisfactionwith the computer-based selection system. Surveyof1,070 respondent fromdifferentprovinces in Indonesiawasconducted.Dataanalysisusingmultipleregressionand factoranalysisshowsthatthefactorsthatinfluencecomputerassistedtestsystemofNationalCivilServiceAgency(BKN)usersatisfactionaresecurityandprivacy,accessibility,awarenessofservice,andtransparency.

ImplementationandAnalysisofReusabilityFrameworkDesignforEventUserInterface

ComponentinPhaser3

AhmadArsyelAbdulHakim,DanaSulistyoKusumo,JatiH.HusenThegrowthofgamedevelopmentisnowwidelysupportedbysoftwaresuchasgameenginesandgameframeworks.Phaser3isapopularHTML5basedgameframeworkonthebrowserplatform.However,Phaser3doesnotfacilitatemanagingcodeofUserInterface(UI)eventcomponent.Sometimesagamedeveloper discovers that UI event component codes, such as buttons or joysticks that have beencreated,must be regenerated on subsequent projectswith the same function. In this research,weproposedareusability framework forPhaser3bycombiningthe factorymethodandthesingletonpatterninmanagingUIeventcomponents.Theaimistomakedeveloperproductivityincreasedsothatthereisnoneedtorepeatthealgorithmorcodethathasbeencreatedpreviously.OurresultsshowthatthePhaser3UIstandardhaslessareusabilityvalueof64,241%,whiletheproposedframeworkthatwasbuilt has relatively higher reusability, that is equal to 84,576%.Gamedevelopers can add thecombination of design patterns, as good software development practices, into an existing gameframeworktoachievecodereusabilitywithoutmajorchangingontheexistinggameframework.Thisapproachcanalsobeusedforthecreatorsofgameframeworktousedesignpatternsfromtheoutsetofdevelopmentofgameframework.

ThePreliminaryStudyonthePerceptionofEngineeringStudentsonBlendedLearning

MinChiLow,ChenKangLee,ManjitSinghSidhu,ZaimahHasan,SengPohLim,SengCheeLimMechanics Dynamics is an important fundamental course in engineering education. However, thiscourseexperiencesahighfailurerateamongengineeringstudents.Thismaybeduetovisualizationproblemsassociatedwithstaticimages,complexengineeringmodels,andconceptmisunderstanding.ThispaperpresentsthepreliminaryresearchofstudentperceptionontheblendedlearningflippedclassroomapproachinovercomingtheirlearningdifficultiesinMechanicsDynamicscourse.Thispilotstudy aims to collect the learningdifficulties of students in theMechanicsDynamics and students'perceptionofblendedlearningusingflippedclassroomapproach.Aquestionnairehasbeendesignedand distributed through an online platform. The sample size is 30 which targets the engineeringstudentsinMalaysia'suniversitywhoalreadytooktheMechanicsDynamicscourseinlessthanfiveyears.Thefindingsareanalyzedusingadescriptivestatisticsapproach.Theinitialfindingsindicatethatthevisualizationproblemisthemainconcernamongthestudents.Althoughthestudentsshowlow awareness regarding the blended learning flipped classroom approach, they have a positiveattitudetowardstheelementoftheblendedlearningapproachtobeimplementedintheirclassroom.


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ImplantSegmentationinRadiographicImageryUsingMultiresolutionMTANNandWaveletDecomposition

RanggaPerwiratama,PranowoPranowo,DjokoBudiyantoSetyohadi

Occasionally implants are coveredwith amusculoskeletal system inmedical imaging, it could bedifficultformedicalpersonnelsaswellascomputerstoperceivetheseimplants.Inthispaper,weuseimageprocessingtechniquestosuppressthemusculoskeletalsystemcontrastinX-rayimagesthatcontainimplantsthroughartificialneuralnetworkscalledmassivetrainingartificialneuralnetworks(MTANN).MTANNisanon-linearfilterthatcanbetrainedusingappropriateX-rayimagesinputandlearningimages.Learningimagesareobtainedthroughtheeditingprocessusingagraphicaleditor,whichisusedtocorrectlearningimagesfromnoise.Toeffectivelysuppressamusculoskeletalsystemthat has multiple spatial frequencies, we use multiresolution MTANN consisting of waveletdecomposition/compositiontechniquesanddedicatedMTANNforeveryresolution.MultiresolutionMTANNhopefullycanprovideimagesthatarereallyclosetolearningimages.ByreducingthecontrastofthemusculoskeletalsystemfromX-rayimages,wewillbeabletocreateX-rayimageswherethemusculoskeletalsystemissubstantiallysuppressedandbeableseetheimplantclearlyincontrast.WeuseMURAasatrainingdatabaseandforvalidationtests.MURAisalargesetofboneradiographicdata,which isdesignedto trainANNtodeterminewhetherX-ray imagesarenormalorabnormal.Once the model is done, when our technique is applied to non-training X-ray images, themusculoskeletal image system is substantially suppressed while maintaining the visibility of theimplant.OurimageprocessingtechniquetosuppressmusculoskeletalsystemsusingmultiresolutionMTANNwillpotentiallybeusefultomedicalpersonnels.ImprovingMulti-ClassMotorImageryEEGSignalsClassificationUsingEnsembleLearning

Method

DeniKurniantoNugroho,NoorAkhmadSetiawan,HanungAdiNugroho

Electroencephalography(EEG)isatechniqueformeasuringelectricalactivityonthescalp.TheEEGdetectsvoltage fluctuationscausedby ioncurrents inbrainneurons.Thebrain-computer interface(BCI)isintendedtoenablehumanstomonitormachinesandinteractwithcomputersthroughtheirbrains. It intends to construct non-muscular production pathways to convert brain function intodiscriminatorycontrolcommandscorrelatedwithvariousEEGsignalsdependentonmotorizedimagepatterns.ResearchonEEGiscurrentlygrowing,especially inthefieldofmotorimaging.EEGsignalprocessingwouldbeafeasibleoptionfordevelopingsuchaBCIdevice.ThefourbasicstagesinclassicalBCI are multi-channel EEG signal acquisition, signal preprocessing, feature extraction, andclassificationofmotorimagepatternsbasedonEEG.Thisstudyaimstodeterminetheeffectofwaveletpacketdecomposition(WPD)andcommonspatialpattern(CSP)featureextractiontooptimizefeatureselectionusingtheensemblelearningmethod.Themethodusedinthisresearchisexperimental,wherethe stages begin with preprocessing, feature extraction with WPD and CSP, classification usingensemblelearningandimplementingfeatureselectionusingtheprincipalcomponentanalysis(PCA)andselectfromthemodel(SFM).Theresultsarethecomparisonoftheaccuracygeneratedfromeachmethod, includingrandomforest(RF)of74.71%,randomforestwithprincipalcomponentanalysis(RFPCA)of68.01%,randomforestwithselectfromthemodel(RFSFM)of82.15%,extratrees(ET)of77.97%, extra trees with principal component analysis (ETPCA) of 64.18% and extra trees withselectedfromthemodel(ETSFM)of83.28%.


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ImplementationandExperimentalCharacterizationofDual-BandWearableReflector

ComposedofAMCStructureforWirelessCommunication

AchmadMunir,DwikiHaryanto,IchsanNusobri,LevyOliviaNur

This paper presents an implementation of dual-band wearable reflector composed of artificialmagnetic conductor (AMC) structureand its characterization throughexperimentalmeasurement.Theproposedwearablereflectordesignedforwirelesscommunicationsisconfiguredby3x3unitcellofAMCstructure.Eachunitcellisconstructedbyacombinationofasquarepatchandasquareringpatch with gaps arranged concentrically. An RT/Duroid RO3003 dielectric substrate with thethicknessof0.5mmisemployedforthedesignaswellastheimplementation.Meanwhile,aprinteddipoleantennaasaninseparablepartofwearablereflectorisalsodeployedonthesamedielectricsubstrate.Thisconfiguration,i.e.,aprinteddipoleantennaandawearablereflector,isexpectabletooperateattwodifferentfrequencybands,ordual-band,coveringtherequiredfrequenciesofwirelesscommunications while maintaining its compact size and simple configuration. Experimentalcharacterization is applied to validate the proposed design, inwhich the results demonstrate theabilityofconfigurationtoworkattwofrequencyresponses,namely2.45GHzbandand3.35GHzband.


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ModifiedPixelValueOrdering-basedPredictorforReversibleDataHidingonVideo

TohariAhmad,AlekNurFatman,AhmadHoirulBasori

Communicationsecurityhasbecomeanissuewheremassivedataexchangestakeplace.Hidingsecretdataincertainfileduringtransmissioncanbeapotentialwaytoprotectthosedata.Nevertheless,someparameters,suchtheresultedstegofilequalityandthedatacapacity,arestillchallenging.Inthispaper,amethodthatmodifiedPixel-basedPixelValueOrdering(PPVO)Predictorisproposedforreversibledatahidingonvideo.PPVOcanembedthepayloadintoapixelwhosevalueisequaltothemaximumorminimumcontextpixel.Toincreasethenumberofpayloadsthatcanbeembedded,wemodifythePPVOmethodbyusingonlybothrightandbottompixelstodevelopthepredictors.Embeddingprocessiscarriedoutiftheconsideredpixelhasthesamevalueasthatintherightorbottom.Theexperimentalresult shows that this method has a significant increase in the embedding capacity and quality.Nevertheless, the overall maximum capacity of each cover is less than some others, while itscorrespondingqualityisstillthehighest.SimulationandAnalysisofPartialTransmitSequenceonPalmDateLeafClippingforPAPR

ValueReduction

Vincent,AntoniusSuhartomo,JoniW.Simatupang,MiaGalinaTheusageofFFTthathasbecomethekeyconceptinOFDMsystemproducesahighPAPRvalue.Inorderreduceit,thereareseveraltechniquesthatcanbeimplemented,suchasthePalmDateLeafclipping,andthePartialTransmitSequence.Previousresearchershaveevaluatedeachtechniqueindividually.Inthisresearch,theAuthorsevaluatethePAPRvalueastheeffectofaddingPartialTransmitSequencetothePalmDateLeafclippingtechnique.Theevaluationisdonewithseveralmodulationtechnique,suchasQPSK,8-PSK,16-PSK,8-QAM,and16-QAM.SincelowPAPRperformanceisnotadvantageousifthesignal'sBERvalueisworsen,thustheevaluationalsoconsiderstheBERperformanceofthesignal.Inthiscase,theAuthorfocusesontheBERperformanceoverAWGNchannel.Theresultshowsthatinallofthescenarios,PTStechniquecouldimprovethesignal'sBERandPAPRperformancefora lowCRvaluesuchas5dBand7dB.Additionally,forthehigherCRvaluesuchas10dBand20dB,thesignal'sBER performance is similar with the normal OFDM signal. Even so, it provides a consistent PAPRreduction of approximately 3 dB. This way, PTS technique always provide improvement in BERperformanceof thesignal.As for thePAPRperformance,PTS technique isable to improveallcasesexceptfor8-QAMand16-QAMsignalwithclippingtechniqueatlowclippingratiosuchas5dB.

DesignAutomationofSinglePhotonCountingMethodforQuantumRandomNumberGeneration

DwiNovazrianto,AlwanMuhamadFajar,MuhammadYusuf,ApriliaKusumaDewi,RiniWisnu

Wardhani,DedySeptonoCaturPutrantoEDU-QCRY1isoneofthequantumkeydistributiondevicesinordertogeneratequantumrandombit.Inthispaper,weconductamechanismtoproduceakeysequencebasedonsinglephotoncountingusingEDU-QCRY1SinglePhotonDetectormoduleforreceivingdatawhichcanbeacquiredusingthereceiver's device automationusing a LightDependentResistor parameter. The design of quantumrandomgeneratorisusingprotocolofQuantumKeyDistribution(QKD)thencomparedtotruetableoftheBB84protocol.Theaveragespeedofthequantumbitintheautomationdeviceis0.0604secondswithestimationfor106quantumrandombitaround16hoursexperiment.Withintherandombits,


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somedifferenceoccurswhenthesenderandreceiverhaveadifferentbasis.Thisresearchprovestheefficiency of automation bit storage on the EDU-QCRY1 Single Photon Detector for generating aquantum random bit and has the valid result as the experiment in performing key distributionsimulationsusingEDU-QCRY1devices.

OntheModificationsofaDigitalSignatureAlgorithmwithSecretSharing

MaretaWahyuArdyaniandUmiUliZulfahCryptographicalgorithmsarevulnerablenotonlyfromattacks,butalsofromtheissuesraisingfromstoringtheprivatekey.Oncetheprivatekeyisleaked,nomatterhowstrongthealgorithmsareclaimedtobe,weareseverelycompromisedandhence,losethesecrecy.Oneofthesolutionstoremedythissituationistoemploythesecretsharingscheme.Thebasicideaofsecretsharingistosplitoneprivatekeyvalueintoseveralsharevalues.Thosesharevaluesarethenstoredintodifferentplacesbydifferentsecretshareholders.Thepresenceofonlyonesharevaluecannotrepresentthewholesecretvalue,andfurther,isunabletoreconstructtheprivatekey.Inthisstudy,weinsertedasecretsharingschemeintoadigitalsignaturealgorithmbasedonconiccurvecryptography.Weoptedtoperformtheschemeonconicbasedalgorithmbecausethesecurvesarebelievedtohavesimplercomputationsthanthoseofelliptic curves.We split the private key value into several share values,which are then utilized togeneratesignaturevalues.Thealterationonsignaturevaluegenerationprocess (byaddingasecretsharingscheme)doesnotaltertheverificationprocesstakingplaceontheoriginalalgorithm(withoutthe secret sharing scheme). We also employed additional mechanism by using the solution ofcongruencesystembasedontheChineseRemainderTheorem(CRT)andFermat'sLittleTheorem.Thewholeprocessisexpectedtoprovideifnotlayersofsecurity,amechanisminwhichwecanmitigatethepossibilityoflosingsecrecy.

ConnectedVehicleCommunicationConceptforFloodLevelWarningUsingLowCostMicrocontroller

MohdFikriAzliAbdullah, SumendraYogarayan,SitiFatimahAbdulRazak,FremontKwong

InMalaysia,therearesomeareasthatfrequentlyhavefloodduetoditchblockageorsuddenrisingofriver'swater.Mostfloodscancausevehiclesandotherinfrastructurestobedamaged.Thevictimsofeveryfloodthatoccurcouldbereducediftheyreceiveanymessagethroughacommunicationmediumtoalertinadvance.Therefore,thisstudyaimstodevelopaprototypesystemtosendamessageonflooddetection via wireless connection using ESP8266 as a means of communication. This prototype isspecifically applied for vehicles to bewarnedmuch earlier. The outcome shows that sending flooddetectionmessagebyusing the communicationmedium is possible and took less than aminute totransmitthemessage.TheflooddetectiondataaresavedinFirebasecloud.

Randomness,Uniqueness,andSteadinessEvaluationofPhysicalUnclonableFunctions

ParmanSukarno,RivaldoLudovicusSembiring,RizkaRezaPahleviThedevelopmentoftheInternetofThings(IoT)canbefoundinvariousplaces.However,multiplekindsof attacks have also increased. IoT devices are very vulnerable to attacks, both physical and non-physical,becauseof theirunmannednature. Innon-physicalattacks, themost important thing is tosecurethedataonmemorydevices.Physicalunclonablefunction(PUF)isthestrongestandlightestmethodtosecuringmemorydevicesandcanbeusedonunmannedIoTdevices.TheadvantageofPUFovercurrentclassicalcryptographytypes is itscompatibilityonIoTdeviceswith limitedcomputingresources. However, before PUF can be claimed to provide security property, it must meet theevaluation indicators: randomness, uniqueness, and steadiness. PUF can be the best solution for


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securingdataonIoTdevicesbecausetheencryptionprocessdoesnotputasecretkeyonthedevice.Instead,thekeyisgeneratedrandomly.ThisresearchisevaluatingtwodifferentPUFchipswiththesamePUFdesign.WedesignedthearbiterPUFontheFPGAandevaluatedtheresultsoftheresponsesgiven.Throughrigorousexperiments,thisresearchsucceededtoevaluatethethreeindicatorsofPUFwheretherandomnessis54.43%:45.4%,and25.88%:74.2%,theuniquenessbetweenchipis69.53%,andlastly,thesteadinessis89.84%,and91.41%.


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ALow-CostHigh-AccuracyThermalCameraUsingOff-the-shelfHardwareDevices

Dinh-TienTran,Viet-KhoaNguyen,Ngoc-ThienNhan,Duc-HiepNguyen,Hoang-AnhPham

TheCovid-19pandemicisspreadingworldwide,thedemandforcontrollingandscreeningfeverisincreasing very rapidly. Some traditional methods like using thermometers or medical checkingcannot handlemany people in crowded areas such as airports, train stations, or buildings. Somethermal imaging systems are used to control people's ins and outs. These systems help manygovernmentsdecreaseandpreventviruses'spreadbyscreeninghumantemperaturebasedonskinradiation.Despitealloftheadvantagesthistechnologyhashad,thissystem'sveryhighcostisonereasontopreventsomeorganizationsfromapplyingitdespitethehighpotentialofimageprocessing,artificialintelligence,andtheInternetofThings.Thispaperwillpresentasystemcombiningalow-cost thermal camera and a standard RGB camera for screening fever combined with attendancechecking.Theexperimentalresultsshowthatthisproposedsystemcanbeappliedinmanyareas,fromuniversitiestobuildings,withtheminimumcostandacceptableperformance.

Sentinel1ClassificationforGarlicLandIdentificationusingSupportVectorMachine

MuhammadAsyharAgmalaro,ImasSukaesihSitanggang,MiaWaskitoThehighdemandforgarlicisnotcomparablewiththeresultsofdomesticgarlicproduction.Indonesiangarlicneedsfulfilledbyimportsupto95%ofnationalneeds.TheMinistryofAgriculturehasaprogramofthecultivationofgarlicinSembalun,EastLombok,WestNusaTenggarainordertorealizegarlicself-sufficiency.ThisstudyaimstoidentifythegarliclandinSembalunusingtheSentinel1Asatelliteimage.Theimageconsistsofdual-polarizationVVandVHvalues.ImageswereacquiredinJulyandNovember2019fortheareaofSembalun,EastLombok,WestNusaTenggaraIndonesia.Preprocessingdatastepsinvolve applying orbits, calibrations, speckle filters, terrain corrections, and linear to dB. SupportvectormachinealgorithmisusedtoclassifySentinel1Aimages.Hyperparametertuningwasdonetogetthebestparameterswhichareregularizationparameter(C)10,gamma1,andtheRBFkernel.Theclassificationmodelhasaccuracyof76%,precisionof71%andrecallof89%.

RecognitionofAcademicEmotionsinOnlineClasses

JordanMingHanPang,TeeConnie,GohKahOngMichaelOnlineeducationhasproliferatedsincetheCOVID-19pandemic.Classeshavebeenmovedonlineasaresultofschoolclosures.Despitetheflexibilityofferedbyonlinelearning,thereareseveralchallengesfaced.Creatingagoodclassroomclimateforonlineclassesisachallengingtask.Itisdifficultfortheteacherstoobtainemotionalfeedbackfromthestudents,especiallyinasynchronousclassesorclasseswithlargenumberofstudents.Itishardfortheteacherstoevaluatetheengagementofthestudentsinclasswithoutknowingthestudents'emotionalresponse.Theexistingfacialexpressionrecognitiondatabasesfocusonbasichumanemotionslikehappy,angry,sad,surpriseandneutral.Thesebasicemotionsarenotappropriateforlearningaspsychologicalandpedagogicalstudieshaveshownthatthere are differences between basic human emotions and academic emotions. In view of theseproblems, thispaperpresentsa studyonacademicemotions.Adataset comprising fourpertinentacademicemotionshavebeenestablished.Empiricalanalysisonthedatasetisconductedusingbothhandcraftedanddeeplearningapproaches.Thebaselineevaluationdemonstratesthesuitabilityoftheestablishedacademicdatasetforonlinelearning.


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ImageSteganographyCompressiveSensingOrthogonalMatchingPursuit

IrmaSafitri

Steganographyistheartofwritingorhidingmessagesinacertainwayonadigitalfile.Thepurposeofsteganographyisthatamessagecanonlybeseenbythetworelatedpartieswithoutbeingnoticedbytheotherperson. In thisstudy,asteganographysystemwasdesignedusing imagemedia tohideasecretmessage.Imagecompressionisfirstperformedusingcompressivesensing(CS)sothatanimagewith a smaller resolution is obtained. Stationary wavelet transforms (SWT) are used as thetransformationmethodandsingularvaluedecomposition(SVD)isusedastheinsertionmethod.Toreconstructorreturnthecompressedmessagetotheoriginalmessage,orthogonalmatchingpursuit(OMP)algorithmisused.Thesystemperformancewastestedusingsalt&peppernoiseattackandgaussiannoise.ThetestresultsshowthattheBERvalueis0.05765andthePSNRvalueis105.666dBin thesteganographysystemwhenattackedwithsalt&peppernoiseatadensitynoiseof0.01. Inaddition,oursystemalsoshowsaBERvalueof0.0509atsigma1andaPSNRof104.615dBwhenGaussiannoiseattacksareapplied.


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IndonesianHoaxIdentificationonTweetsUsingDoc2Vec

TitiWidaretna,JimmyTirtawangsa,AdeRomadhony

Inthispaper,wepresentourworkonhoaxdetectiononacollectionofTweets.Wetacklethehoaxdetectionasatextclassificationproblem,withDoc2VecasthetextrepresentationmethodandSVMastheclassifier.Wecollectedandannotated5000Tweetsthatconsistof2500hoaxTweetsand2500truthTweets.Theexperimentalresultsshowthat theaccuracyofourproposedhoaxdetectiononTweetsis93.4%.

ElectronicNoseDatasetforClassifyingRiceQualityusingNeuralNetwork

FerdyErlangga,DedyRahmanWijaya,WawaWikusnaRice is a staple food ingredient because it is themain food element for Indonesia and theworld.However,thequalityofricecandeclineovertimeuntilitbecomesexpiredorsmellyandcannotbeconsumed.Atpresent,theconventionalmethodtodistinguishbetweenexpiredriceandnotexpiredriceisstillcarriedoutbyobservingricewiththehumansenseofsmell.However,thismethodisstillconsideredineffectivebecausethehumansenseofsmellcanchangeduetochangesinbodyhealth.Inthis case, this studyusesanelectronicnose (e-nose) andamachine learningneuralnetwork (NN)algorithmtodetectriceconsistency(expiredandnon-expired).Thedatasetwasobtainedfromthee-nosebyrecordingsensorinformationfor25weeksbystoring48.486totaldataand2.017datarecordsforoneweek.The results of the classificationusingNNarewith anaccuracy scoreof99.84%, theproposedmethodsuccessfullyclassifiedricequality.

SVMParallelConceptTestwithSMODecompositiononCancerMicroarrayDataset

RahmatRamadanPrasojoe,Setyorini

Support Vector Machine (SVM) is a reliable method for performing classification and regressionespeciallyinsupervisedmachinelearning.However,SVMhasscalabilityissuesincomputetimeandmemoryusage.Therefore,therearemanyproposalsforParallelSupportVectorMachine(PSVM)formining large-scale data. In this study, the authors conducted a PSVM concept test with SMOdecompositionthatcouldbehandledandclassifiedcancerusingmicroarraydata.Theauthorappliesthe Sequential Minimal Optimization (SMO) technique which uses LaGrange multipliers to solvequadratic programming (QP) problems that arise during training. To test the concept of SMOdecomposition,thedatasetwillbebrokendownintoseveralsubsetsandthenindependentlyconductSMO training foreach subset andcombineeach training result intooneSMOclassificationmodel.Evaluation is done by comparing the accuracy and performance of SMO decomposition and non-decomposition SMO. Evaluation result are accuracy of SMO decomposition 75% and non-decompositionSMO63%,andaswellasSMOdecompositiontrainingtime5.7timesfasterthantonon-decompositionSMO.

DetectingOnlineRecruitmentFraudUsingMachineLearning

HriditaTabassum,GitanjaliGhosh,AfraAtika,AmitabhaChakrabarty


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OnlineRecruitmentfraud(ORF)isbecominganimportantissueinthecyber-crimeregion.Companiesfinditeasiertohirepeoplewiththehelpoftheinternetratherthantheoldtraditionalway.Butithasgreatlyattractedscammers.Inthispaper,wehaveproposedasolutiononhowtodetectORF.Wehavepresentedourresultsbasedonthepreviousmodelandthemethodologies,tocreatetheORFdetectionmodelwherewehaveusedourowndataset.WehavecreatedourdatasetbasedontheBangladeshjobfield and by using a publicly accessible dataset as a reference. Furthermore, Logistic Regression,AdaBoost, Decision Tree Classifier, Random Forest Classifier, Voting Classifier, LightGBM, GradientBoostingarethealgorithmsthathavebeenused.Wehavefoundtheaccuracyofdifferentpredictionmodels, where LightGBM (95.17%) and Gradient Boosting (95.17%) give the highest accuracy.Throughthispaper,wetriedtocreateaprecisewayfordetectingfraudulenthiringposts.

DataMiningforRevealingRelationshipbetweenGoogleCommunityMobilityandMacro-economicIndicators

Gunawan

Googlecommunitymobilityreportshavehelpedtoevaluatetheeffectivenessofgovernment-imposedmovement control among countries. However, the relationship between themobility data and thecharacteristicsofregionsislessreported.ThisstudyaimstorevealhiddeninformationfromGooglecommunity mobility reports and relate them to all 34 Indonesian provinces' macro-economicindicators.ThissecondaryresearchimplementsadataminingapproachusingtheCRISP-DMprocessframeworkandKnimeAnalyticsPlatform.ThecommunitymobilitydataofresidenceandworkplacearecollectedasatimeseriescoveringFeb16,2020,toJan31,2021.Macro-economicindicatorsarecollected from thewebsite of the Indonesiannational statistics agency. The clusteringmethodhasgroupedprovincesintothreebasedontheirmobility.ThefindingsindicatetherelationshipbetweenmobilityfluctuationduringtheCOVID-19pandemicandmacro-economicindicators,namelyhumandevelopment indexand labor forceparticipationrate. In the theoreticalaspect, thisstudyhasbeeninitiatingtheinvestigationofcommunitymobilityandmacro-economic.Policymakersindealingwithpost-pandemicrecoveryplanningmightconsidertheclustercharacteristicsforbetterplanning.


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SuitableKnowledgeManagementProcessImplementation:ACaseStudyofPTSinergiSentra

Digital

YusufPratama,DanaI.Sensuse,SofianLusa,DamayantiElisabeth,NadyaSafitri,GhanimKanugrahan,BryanzaNovirahman

Somecompaniesnowadaysthatarenotfocusedoninformativetechnology(IT)developmenthavenotyet implementedaknowledgemanagement (KM)processwhich canbe seen from the lackofKMsystemfromtheapplicationthattheyhave.ThiswillthenleadtotheITdivisioninthecompanyhavingtodealwithstruggleinextracting,reading,aswellasreviewingmanykindsofdocuments.PTXYZ,oralso known as Port Cities Indonesia, is one of the company exampleswhichmainly focused theirbusinessondevelopingopen-sourceenterpriseresourceplanning(ERP)fortheothercompanies.Itisexpectedthatimplementingoneoftheperfectknowledgemanagementprocessesthatfitwiththecompanycansupporttheuseofmaintainingtheirknowledgeduetosomeintegrationtoitsdata.ThecontingencyfactortheorywasusedinthisstudytoobtaintheKMprocessneeds.Basedontheresultsof interviews with employees from each division involved, this study finds that Socialization forKnowledgeSharingisthemostprioritizedcontingencyprocesswithchattingandsharingmediatomanifestthatprocess.

CriticalSuccessFactorsforProjectTrackingSoftwareImplementation:ACaseStudyataBankingCompanyinIndonesia

HendroPrabowoHadi,RidhaEryadi,TeguhRaharjo

Intoday'sbusinessenvironment,locationisnotanobstacleanymore.Teamscanworkvirtuallyandcollaboratively with ease using web-based project tracking software. As a result of computertechnologyandinformationflowadvances,companiesaremovingtonewprojectmanagementtools,improvedprojectgovernance,andincreasedstakeholderengagement.Tokeepitscompetitivenessandabilitytodeliverproductsonschedule,oneoftheBankinIndonesiaadoptedJiraAtlassianasprojecttrackingsoftwaretoreplacethepreviousin-housesystemwhichiscallede-SDLC(electronicSoftwareDevelopmentLifeCycle) thatcanmonitorandtrackprojectstatus.But itdidnotgowellandfacedobstaclesatthebeginningofimplementation.ThisstudyaimstofindandevaluatetheCriticalSuccessFactors (CSF) for project tracking software implementation in one of Indonesia banking company.From previous research, there are identified challenges and obstacles when applying projectmanagement tools which are governance, adoption process, technology & knowledge, andstakeholders.Fromthisresearch,weidentified4keyscriteria,namelypeople,process,organization,and technicalasCSF (ordered fromthemost importantcriteriausinganalyticalhierarchyprocess-AHP).Thefindingsofthisstudycouldhelpcompanyincreasingthesuccessprobabilityinimplementingprojecttrackingsoftware.

AssuranceCasePatternusingSACMNotation

NungkiSelviandro

TheStructuredAssuranceCaseMetamodel(SACM)isametamodelandspecificationthatcanbeusedtorepresentstructuredassurancecases.Anassurancecaseisanapproachforanalysing,documenting,and communicating a clear structured argument and evidence about a particular systemwithin aspecific environment and circumstances. SACM provides abstract syntaxwith a set of features todevelop assurance cases, including supporting the development of an assurance case pattern. A


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pattern in the assurance case development context is useful, for example, as an approach forabstractingthedetailsoftheargument,andwhenpossible,itcanbeusedforthedevelopmentofotherargumentsbyinstantiatingthepatterninaspecificdomainapplication.TosupportthedevelopmentandadoptionofSACM,wehavedevelopedSACMNotation(SACMN)asaconcretesyntaxthatconsistsofvisualvocabulariesandcompositionalrules(asvisualgrammar).ThedevelopednotationhasbeenincludedaspartoftheSACMstandard2.1updateversion.Inthispaper,weintroduceanddiscusstheapplication of the assurance case pattern using SACMN to support the SACM adoption in thedevelopmentofassurancecases.

SustainabilityAndAptnessOfGameElementsInAGamifiedLearningEnvironment

MageswaranSanmugamSustainingtheinterestlevelofstudentsinanyformoftechnology-basedlearningposesasignificantchallenge. Infusion of any tech-based elements into learningmay not be sufficient enough for theGeneration-Zstudents,whoselivesrevolvearoundtechnology.Althoughgamification,gameelementsinnon-gamingcontextsaremorecomfortablebeingimplementedbyordinarypeoplewithnotech-based knowledge.Nevertheless, in the context of learning, the sustainability of the game elementsneedstobeidentified.Therefore,mixed-methodresearchwasconductedon28studentsaged13yearsoldfromanurbanschoolinMalaysiafor14weeks.Thestudentswereintroducedtoagamifiedlearningmethodthatinfusedgamificationelementsinthetraditionalclassroomandtheonlineclassroom.Thegame elements testedwere points, badges, and leader boards. Studentswere taught two separatetopics in theMalaysian Science syllabususing gamified learning to ensure the students' continuityeffects.Uponcompletion,thegameelements'finaltallywasassessedandsupportedbytheinterviewfeedbackfromthetop3studentsfromeachgameelement.Basedonthefindings,gamifiedlearningwithgameelementshelpedreducedboredom,andusingtechnologymadelearningfun.Althoughsomerespondentssharedthefearofcomplacencyofusinggamesinlearningandthemixedresponsewasreportedwhenitcametothepreferredtypeofgameelementsortypeoflearningthatsuitsthegameelements.

UserInterfaceModelforVisualizationofLearningMaterialsinComicStripFormUsingGoal-

DirectedDesignMethod

MuhammadFauzanNurAdillah,DanangJunaedi,YanuarRahmanInalearningprocesstohelpstudentsrememberandunderstandthematerial,oneeffortcanbemadeistoprovideessentialpointsfromamaterial.Studentsarelessinterestedindeliveringessentialpointsbasedontextbecausetheyareconsideredmonotonous.Nowadays,manystudentstendtoreadcomicbooksratherthanbooks.Comicscanwashawaytheemotionsofreadersinthestoryline.Therefore,weneedsupportingmediatohelpteachersvisualizeessentialpointsinthebookincomicform.Sothattohelpteachersinthisregard,auserinterfacemodelisneeded.ThisresearchwasconductedwithaGoal-DirectedDesignapproachtogainuserperspectiveindevelopingsupportingtoolstovisualizethematerial'scrucialpointsintheformofcomicstrips.Testingtheresultingmodelwascarriedouttwiceandeachtestinvolved15respondentstogetmaximumresults.BasedontheevaluationresultsusingtheUSEQuestionnaireevaluationmethodfortheprototypemadeandtheresultswereintheexcellentcategory.Thus,theresultinguserinterfacemodelisasupportingmediumforteachersindeliveringlearningmaterialincomicformfollowingtheexpectedobjectives.


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ABSTRACTSESSION4E:Networking,IoT,andSecurity

AccessibilityandResponseTimeAnalysisontheCOVID19WebsiteinIndonesia

RyanWicaksonoandHilalH.Nuha

Ping is a computer network utility software used to test the reliability of hosts on IP (InternetProtocol).Thisisusedtomeasurethetimesentfromthehosttothedestinationcomputerorcentralcomputer.PingoperatesbysendingtheInternetControlMessageProtocol(ICMP).Tracerouteisusedtoshowtheroutethepacketpassestoreachthedestinationorroutediagnostictool.Thisprocessisdone by sending the message Internet Control Message Protocol (ICMP) Echo Request_ to thedestinationwiththevalueofTimetoLiveorcommonlyreferredtoasTTLwhichisincreasing.Therouteshownisa listofrouter interfaces foundonthepathbetweenthehostanddestination.Thepurpose of pinging and traceroute is to find out the status of up or down in the network. Withtraceroute,wecananalyzeinformationaboutthelocationoftherouter.

ModifiedBitParityTechniqueforErrorDetectionof8BitData

FakhiraZulfira,HilalH.Nuha,DodiWisaksonoSudiharto,RioGunturUtomo

Bitparityisoftenusedasanerrordetectiontechniqueinsendingdigitaldatathathasinterruptionduringtransmission.Errordetectiontechniquesallowcorrectingdatatobeerror-free.Researchthathasbeenusedusingbitparityhasbeendonebutonlydetectedevenparityandoddparitywheretheprocessisverysimplesothatmodificationscanbemadetodetectotherthanevenoroddparity.Inthisstudyamodificationofthebitparitymethodwasbuiltinwhicheachmessagereceivedinthefirst4bitswillbedetectedwhether thebitvalue is amultipleof4and the remainingbitswillbeevenlydetected.Errordetectionusingtheproposedparitybitsresultsinafairlygooddetectionsystemandismoreefficientiftherearemorebitsinthemessage.IoTApplicationonAgriculturalAreaSurveillanceandRemote-controlledIrrigationSystems

RatnasariNurRohmah

This research applies IoT technology to help farmers overcome two problems by proposingagriculturallandsurveillancesystemandremote-controlledirrigationsystems.Theagriculturallandsurveillancesysteminthisresearchisdesignedtodetectobject'smotion,takepicturesofobjectsandsendimagedatatotheuser'ssmartphone.Thissurveillancesystemalsoprovidesa livestreamingvideomodebyrequest.Theirrigationsystemisdesignedtomonitortemperature,senddatatouser,andallowing to remotecontrol submarinepumpoperationby theuserviaa smartphone.Testonsystemsperformanceshowedthatsystemperformedproperly.Inthesurveillancesystem,theoptimaldistanceformotiondetectionbythesensorisin6meters.Onsunshineday,timeintervalfromimagetakenwhen somemotion detected and notification received by the userwas 4.9 seconds, whilstinterval time fromnotification to image sentwas3.9 seconds. In live streamingvideomode,usershouldwait2.3secondsinaveragetoreceivelivestreamingvideoonsmartphone.Intheirrigationsystemperformance,thesensormeasuredaveragetemperaturemeasurementerroris1.49%.Sensordata transmission to user's smartphone works well and user can remotely control the pumpoperation.


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Present-80EncryptionAlgorithmImplementationonGPRSArduinoMega-2560CyberPhysical

TrackingSystem

DwiNovazrianto,RiniWisnuWardhani,NaufalHafizSyahidanCyberPhysical System(CPS) canbe characterizedasaphysical systemandanengineering systemwhich can be monitored, controlled, coordinated and integrated processes with computing andcommunication. Themore development ofmobile internet technology, themore vulnerabilities ininformation security. In the information age, cyberattacks are a challenge. Increasing security onCommunicationdevicecanmaptheactuallocationoftheGPSintoadifferentrepresentation.VTSwillsendvehicle locationdata to theserverorsystemuserperiodically.TheVTSdeviceconsistsof theArduinoMega-2560andthePRESENT-80algorithm.TheGPSmoduleservestoprovidelocationdata,and the sim800L v2 module as a communication module with GPRS services. PRESENT-80implementationontheVehicletrackingsystemwassuccessfullycarriedout.LocationdatacanbesentinciphertextformandlocationdatacanbedecryptedinanAndroid-basedmonitoringapplication.

HuntingCyberThreatsintheEnterpriseUsingNetworkDefenseLog

ArdianOktadika,CharlesLim,KalpinErlanggaAs business continues to evolve, the systems that support its functionalities also becomes morecomplex and sophisticated. This situation constitutes even larger cyber security threats, henceincreasedbusinessrisksthatrequiresmoresecurityprotectionforthebusinesssystems.Firewalls,IntrusionDetectionSystemandWebApplicationFirewallshavebeenutilizedextensivelytocounterthesethreatsattheperimeterbetweentheInternetandinternalsystems.Unfortunately,thesesecurityperimeter devices failed to detect new unknown threats due to its reliance on signature-basedmethods.Inthisresearch,weproposeaframeworkthatemploysthreathuntingthatutilizethesecuritydevice log and network forensics to uncover the unknown threats and validate the results usingmemoryforensics.Newrulescreatedfromthisdiscoverywillallowtheprotectionatthebordertobemoredynamicandeffectiveinhandlingpreviouslyunknownsecuritythreats.


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BuildinganIDCardRepositorywithProgressiveWebApplicationtoMitigateFraudbasedon

theTwelve-FactorAppMethodology

KevinAkbarAdhiguna,FirhanMaulanaRusli,HendyIrawan

Dataandinformationhavebeenapartofhumanlife.TheyaremostlyaccessedthroughtheInternetandcanbeprovidedinvariouswayssuchastexts, files,andimages.Nowadays,a lotofpeopleusepersonalcomputersandmobilephonesandrelymoreoncloudcomputingservicestostore,backup,and share data. To cut down the probability of potential fraud, ID Card of users are stored in arepositoryinordertobeverified.TheIDCardrepositoryismeanttobeusedbothonmobilephonesanddesktopcomputers,sotheconceptofprogressivewebapplication(PWA)isutilized.Therefore,the ID cardPWA should fulfill cloud-native standards in order to gainmaximumbenefits of cloudcomputing. In this paper,wewill discuss a recommendation on how to develop a PWA using theTwelve-Factor App methodology and deploy it to a cloud infrastructure with cloud-relatedtechnologiessuchasDockerandAmazonWebServices(AWS).

XB-Pot:RevealingHoneypot-basedAttacker'sBehaviors

RyandyDjap,CharlesLim,AndiYusuf,KalpinErlanggaThedevelopmentofDeceptiontechnologysuchasthehoneypotispredictedtocontinuetotakeasharpdip.Wherethismakesperfectsenseduetothelargevolumeofattacks,complexityanddefenseevasionmanoeuvres. This causes the use of honeypot to becomemore prevalent in addition to deceptingserviceswhicharepopularlyattackedbyattackers.Honeypotsarealsousedtostudymethodsattackersusetocompromisecertainservices.Itishopedthattheaimoftheattackercanbeknownandafterthemappingcanbesearchedforattackpatternsthatcanbeusedasaruleto lookforpatternsthatareterminatedwhen the patterns found are similar to those in Honeypot. This research aims to takeadvantageofthehoneypotintermsofmappingtheattacksthatthehoneypothascollectedinordertoknowthetechnique,tacticandintentionofeachcommandthatislaunchedagainstthehoneypot.Apartfrommappingcommands.Thepurposeofthispaperistoprovideinsighttoreadersaboutthethreatsthat are rife with being modeled with statistical explanations using high volume and long tail. Inaddition, this research is a continuation of our previous paper where this research contained anenhancementframeworkandaddedhoneypotdatatypestounderstandbroaderattacks.DesignofaSnort-basedIDSontheRaspberryPi3ModelB+ApplyingTaZmenSnifferProtocol

andLogAlertIntegrityAssurancewithSHA-3

GarandYudhaandRiniWisnuWardhaniTheCOVID-19pandemichasforcedmanypeopletodoworkfromhome(WFH).Inpractice,networksusedinhomesgenerallydonotapplysecurityandlackawarenessofpossiblecybersecuritythreats.The development and use of security systems on home networks with inexpensive and practicalsolutionsarehighlydemanded.Therefore,adevicethatcandetectcybersecuritythreatsisneededtominimizetheriskswithpracticalsolutionsandaffordablecosts.ThisconductedresearchprovidesadesignofaSnort-basedIntrusionDetectionSystem(IDS)devicethatwasappliedtotheRaspberryPi3ModelB+.TaZmenSnifferProtocol(TZSP)isalsoimplementedtoanalyzenetworktrafficandTheSHA3algorithmusedtocalculateperiodicalhashvalue.Thispaperimplementsfivetypesofattacks,thereare ICMPBlackNurse, SYNFlood, SMTPBruteForce,RDPBruteForce, andWebPhishing. Inaddition, this research using the 'htop' program to perform performance testing, and 'sha3sum'


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programtoconductthetestvectoralgorithm.Thesystemhas100%accuracyratedetectingtheattacksandrequireslessthan50%CPUand10%RAM.Sothatthesystemcanbeappliedtohomenetworksasapracticalandaffordablecostsolutioninordertoimplementcybercrimesecurity.

LearningMethodofPerformance-orientedCongestionControl(PCC)forVideoStreamingAnalysis

RezyNoerdyah,HilalH.Nuha,SidikPrabowo

TransmissionControlProtocol(TCP)congestioncontrolarchitecturesuffersfromperformanceissuesthat are not optimal. Thus making the TCP and the variations have little hope of achieving highperformance.ThisisduetotheuseofhardwiredTCPmappingwhereeacheventhasbeenassumedtobeaspecificdisruptionandTCPmustcopewiththeincidentwithoutunderstandingtherealconditionofthenetwork.Thisassumptionresultsinperformancedegradation.Performance-orientedCongestionControl(PCC)isanewcongestioncontrolthatmakeseverysenderobservingtheactionandnetworkperformanceempiricallytobeabletotakeactionthatyieldshighperformance.PCChasbeentestedinseveralcases.Onecaseisvideostreaming.Theexperimentisdesignedtoobtaintheperformanceforvideostreamingintermsofthroughput,delayandpacketlossforthePCCandtheTCPtodeterminebetterperformanceresults.ResultsfromthetestingofeachmetricinwhichTCPandPCCthroughputsare 1064.841 and 150.825 kbps respectively. Delay of TCP and PCC are 5.326 ms and 3.843 msrespectively.PacketlossofTCPandPCCare0.905%and0.016%,respectively.SothePCCachievesgoodperformancesontheparametersofdelayandpacketloss.WhereastheTCPisshowntoperformbetterintermsofthroughput.

ExperimentalInvestigationofWaveAbsorberMadeofRingResonator-BasedAMCStructure

LevyOliviaNur,IchsanNusobri,BudiSyihabuddin,AchmadMunir

Thispaperdealswithanexperimentalapproachforinvestigatingcharacteristicsofanelectromagnetic(EM)waveabsorbermadeofartificialmagneticconductor(AMC)structure.AnarrayofringresonatorsinsymmetricalshapeusedasthebasisofAMCstructureisdesignedtoabsorbtheincomingEMwavesatacertainoperatingfrequency.Theproposeddesignofwaveabsorbermadeofringresonator-basedAMCstructureisdeployedonaFlameRetardant(FR)4Epoxydielectricsubstratewiththethicknessof1.6mm.Theshapeanddimensionof ringresonatorareoptimized toworkat thecertainoperatingfrequencyusingaunit cellwithproperboundaryconditions.To investigate theabsorptivity rateofproposed wave absorber, resistive elements are incorporated into the ring resonator. Here, theexperimentalmeasurementaimstoverifytherealizedwaveabsorberwiththeproposeddesignandtomeasure the absorptivity characteristics of realized wave absorber. The measured results for therealizedwaveabsorberwhichtakesthedimensionof220mmx220mmhavetheabsorptivityrate,intermsofreflectioncoefficient(S11),of-16.65dBand-25.72dBforthewaveabsorberwithoutandwithresistors,respectively.TheseresultsarecomparabletothesimulatedoneswiththeS11valuesof-18.01dBand-23.41dBforthedesignwithoutandwithresistors,respectively.


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ABSTRACTSESSION5B:DataScience

InformationCascadeMechanismandMeasurementofIndonesianFakeNews

AndryAlamsyahandAslaSonia

Thehighnumberofsocialmediaactorshasthepotentialtoproducefakenews.Fakenewscouldbemotivatedbyvariousagendas,suchaspolitics,government,andhealth.Therefore,weneedtoknowhow themechanism andmeasurement of the spread of fake news.One approach to studying thespreadof it is the informationcascade.Thispaperwillmodel the informationcascademechanismusing Social Network Analysis (SNA) and Susceptible-Infected (SI) model. SNA is adopted toinvestigatethespreadingmechanismanddeterminetheproportionofactorsexposedtofakenews.SIisappliedtomeasurethespeedoftransmissionoffakeandtruenews.Usingseveraltopicsamples,theresultsallowustounderstandthemappingofcascadeinformationfromfakenewsbyalevelthatdifferentiatesthenodelevelfromthesourcenewstotherestofthenodesinthenetwork.Ourfindingfakenewscanreach0,6414morefractionsandspread4,6timesfasterthantruenews.

FraudAccountsIdentificationModellingonMulti-PlatformE-Commerce

GrawasSugihartoandYudistiraAsnarNowadays,cybercrimeisincreasinglyprevalentinsociety.BasedondatacompiledbytheIndonesiaNationalPolice,thenumberofcybercrimesincreasesby6.46%annually,withonlinefraudasthemostreportedcrimewith7.892casesor44.40%outofthetotalcaseshandled.Themodusoperandiinonlinefraudprimarilyusesmanipulatedprofileaccounttogainthevictims'trust.Therefore,itisnecessarytohavea commondetectionmodel for fraudaccountsonmulti-platforme-commerce toavoidonlinefraud. This research uses theNaïve Bayes classification, Decision Tree, and K-NN as themodelingalgorithms. The classification test result showed that the optimal performance with the highestaccuracydiffersamongtheplatform.ThegreenplatformreachesthehighestaccuracyusingtheK-NNalgorithm (90.51%), the red platform went to the optimal performance using the Decision Treealgorithm(96.89%),andthemulti-platformreachedtheoptimalperformanceusingtheNaïveBayesalgorithm(90.02%).ClassificationonParticipantsRenewalProcessinInsuranceCompany:CaseStudyPTXYZ

DeddyUtomo,NoperidaDamanik,IndraBudi

Insuranceisaformofriskmanagementandoneofthefastest-growingbusiness.PTXYZisacompanythatfocusesonhealthandlifeinsurance.OneexcellentproductownedbyPTXYZisManagedCare(MC)Insuranceanditdominate64.5%ofthecompany'spremiumincome.However,MChasahighclaimratiovalue.Provenbytherewere363companiesthathaveaclaimratioofmorethan76%in2020.Theincreasingoftotalclaimratioisduetothecompanyhasnotbeenabletopredicttheclaimratiowhentherenewalcompanyapplyforaninsuranceparticipant.Thisstudyfocusesonclassifyingparticipantson insurance renewalprocess so that company canbemore selective to approve theparticipants.Participantselectioncanhelpcompanytoreduceclaimratio.Proposedmethodisdoingclassificationoninsuranceparticipantsdatausing3803datasetswith4attributesandfivealgorithmsandfindsignificantfeatureswhengeneratingthemodel.Themodelswillbevalidatedusingk-foldscross-validationwithk=10,evaluationresultsshowtheaccuracyofeachalgorithmasfollowing,NaïveBayes70.00%,SupportVectorMachines67.00%,DecisionTree95.40%,LogisticRegression90.20%,andNeuralNetworks79.30%.TheresultsofthestudyrecommendDecisionTreealgorithmwithanaccuracyof95.40%fortheclassificationofrenewalcompanythatwilljoinasinsuranceparticipants


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because it has a better accuracy value than other algorithms. Decision Tree shows that themostsignificantfeaturesindefiningprospectivecompanyassessmentistheaverageofage.HybridSpace-TimeModelandMachineLearningforForecastingMultivariateSpatio-Temporal

Data

HendriPrabowo,DedyDwiPrastyo,SetiawanThepurposeofthisstudyistoproposeahybridmodelbycombiningstatisticalmethods,namelyTimeSeriesRegression(TSR),MultivariateGeneralizedSpace-TimeAutoregressive(MGSTAR)asaspace-timemodel,andMachineLearning(ML)toforecastmultivariateSpatio-temporaldatasimultaneously.Thelinearmodel,namelyTSRisusedtocapturetrendsanddoubleseasonalpatterns.MGSTARisamodelforcapturingdependenciesbetweenlocations.Meanwhile,capturingnonlinearpatternsusedtheMLmodel. In this study, three types ofMLmodel is used, i.e., Deep LearningNeural Network(DLNN),FeedForwardNeuralNetwork(FFNN),andLongShort-TermMemory(LSTM).Weapplythisproposedmethodtosimulateddata.BasedontheRootMeanSquareError(RMSE)value,theproposedhybrid methods, namely TSR-MGSTAR-DLNN, TSR-MGSTAR-FFNN, and TSR-MGSTAR-LSTM,outperformothermodelssuchasTSR,MGSTAR,MGSTAR.-DLNN,MGSTAR-FFNN,MGSTAR-LSTM,andTSR-MGSTAR,especiallywhenthedatacontainnonlinearnoisecomponents.TheresultsalsoshowthattheproposedhybridmodelcantacklecomplexpatternsonSpatio-temporaldatacontainingtrends,doubleseasonal,linearnoise,nonlinearnoise,anddependenciesbetweenlocations.

ComparativeStudyofCovid-19TweetsSentimentClassificationMethods

UntariN.Wisesty,RitaRismala,WiraMunggana,AyuPurwariantiCovid-19 isadiseasecausedbyavirusandhasbecomeapandemic inmanycountriesaround theworld.Thediseasenotonlyaffectspublichealth,butalsoaffectsotheraspectsoflife.Peopletendtowritecommentsaboutthingshappeningduringthepandemiconsocialmedia,oneofwhichisTwitter.SentimentanalysisonTwitterdataisnotaneasytaskduetothecharacteristicsofthetweetertextwhichisusergeneratedcontent.Therefore,inthispaper,asentimentanalysisstudyiscarriedoutonTwitterdatausing threeschemes,namely thevectorspacemodel (BagofWordsandTF-IDF)withSupportVectorMachine,wordembedding(word2vecandGlove)withLongShort-TermMemory,andBERT (Bidirectional Encoder Representations from Transformers). Based on the conductedexperiments,BERTachievedthebestperformancecomparedtotheothertwoschemes,reaching0.85(weightedF1-score) and0.83 (macroF1-score) for the classificationof three sentiment classes onKagglecompetitiondata(CoronavirustweetsNLP-TextClassification).


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CountDataForecastingusingPoissonAutoregressionforCOVID-19CasePredictioninJakarta

BahrulIlmiNasution,YudhistiraNugraha,JuanKanggrawan,AlexLukmantoSuherman

COVID-19iscurrentlybecometheglobalproblem,includinginJakarta,Indonesia.TherehavebeenmanyapproachestopredictingtheCOVID-19occurrence.Oneoftheoccurrencesisusingforecastingapproach.Thetraditionalforecastingmethod,particularlymachinelearning,oftendoesnotconsidertheconditionofthedata,althoughithasformsofthecount,suchasthenumberofcases.ThisstudyaimstoemployanautoregressionmodelusingPoissondistributioninpredictingtheCOVID-19futurecases, namely the positive and recovery number. We compare the Poisson Autoregression withseveralwell-knownforecastingmethods.ThisstudyfoundthatPoissonAutoregressioncouldcreateanaccuratepredictionandtendtofollowstherealdataforthenext8to14daystothefuture.ThisapproachcanbeusedtoforecastthefuturecasesofCOVID-19,andothercasesthatusecountdata.OptimizationofCropsAllocationPlanninginCianjurInvolvingWaterCostConstraintsUsing

GeneticAlgorithm

BambangWahyudi,IrmaPalupi,SitiSa'adahTo become urban agriculture, Cianjur needs to create land-use planning base on its agriculturalsituation.Inthiswork,anoptimizationmodeltocomputeoptimalcropallocationsforeachsub-districtis proposed and solved by a genetic algorithm (GA) to find the best solutions. The land utility isformulatedasthemarginbetweenthepossiblecostandrevenuebaseonthedatacollectedfromtheIndonesian Central Bureau of Statistics data. The result analysis can support decision-making forallocationcropproductionplanninginpracticetoincreaseagriculturalrevenueintheCianjurregency.Ageneticalgorithmisusedtofindtheoptimalsolutionthatmaximizesthemarginfunctionwithsomerealisticconditionsobtainedfromthegovernment'sallowedlandrestriction.Thecomputationresultsshowthatthehighestoptimalvalueis30.65onthelogscale.Thefitnessconstanttothisasymptoticriseisafter17generations,sothatitconvergesquicklyforsomeexperimentalGAparametersetting.Intheoptimalallocationresults,thesweetpotatogainsthesmallestportion,followedbycassavathatis only around2%of the total plantation area.Meanwhile, the largestportionof landallocation issuggestedtobecorn,basedontheoptimalsolutionreturningthemaximumprofitmargin.Forallcrops,theCidaunsub-districtappearstobethemostexpansivelandareainCianjur.

FakeNewsDetectionwithHybridCNN-LSTM

KianLongTan,ChinPooLee,KianMingLim

In thepastdecades, informationandcommunicationtechnologyhasdevelopedrapidly.Therefore,socialmediahasbecome themainplatform forpeople toshareandspread information toothers.Althoughsocialmediahasbroughtalotofconveniencetopeople,fakenewsalsospreadmorerapidlythanbefore.Thissituationhasbroughtadestructiveimpacttopeople.Inviewofthis,weproposeahybridmodelofConvolutionalNeuralNetwork(CNN)andLongShort-TermMemory(LSTM)forfakenews detection. The CNN model plays the role of extracting representative high-level sequencefeatureswhereastheLSTMmodelencodesthelong-termdependenciesofthesequencefeatures.Tworegularizationtechniquesareappliedtoreducethemodelcomplexityandtomitigatetheoverfittingproblem.TheempiricalresultsdemonstratethattheproposedCNN-LSTMmodelyieldsthehighestF1-scoreonfourfakenewsdatasets.


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AspectBasedSentimentAnalysisWithCombinationFeatureExtractionLDAandWord2vec

RizkaVioOctrianyInggitSudiro,SriSuryani,YuliantSibaroni

Aproductreviewisneededbyacustomerbeforehebuysaproduct.Currently,severalplatformscanbeused toprovideproduct reviews, oneofwhich is thebeautyproduct. Every customer can readbeautyproductreviews,notonlyfromoneaspectofthereviewbutitcanbefromseveralaspectsofthereview.itisdifficultforconsumerstofindallthereviewsfromvariousaspectsquickly.Therefore,inthisstudy,acombinationofLDAmodelingmethodsandWordEmbeddingWord2vecwereused,toobtainsentimentsfromeachofthepredeterminedaspectsofthereview.Inthisstudy,theaccuracyofthecombinationofLDAwillbecomparedwiththeWord2vecSkip-gramandContinuous-bag-of-word(CBOW)models.Fromthe twocombinations, it is foundthat thecombinationaccuracyofLDAandWord2vecSkipgramis80.36%,andforCBOWisonly74.37%.Meanwhile,theSVMandK-FoldCross-Validationalgorithmsareusedtofindtheaccuracyofsentimentpredictionsontheaspectsofprice,packaging,andfragrances.Comparedtotheothertwoaspects,thepackagingaspecthasthehighestaccuracyat89.71%.

SentimentAnalysisonBeautyProductReviewsusingLSTMMethod

MuhammadRafiiDanendra,YuliantSibaroniA review isanopinion that containsvalueon the joboreventbeing reviewed.Manysitesprovidereviews of products or goods in themodern era to users, such as the femaledaily.com site,whichprovidesaplatformforuserstoreviewproductspurchased.Thesentimentscontainedinthesereviewsarevaluableinformationforbusinessowners.Thankstoproductreviews,businessownersgetinsightsanddatarelatedtotheproductstheyselltoimprovetheirproducts'quality.However,gettingopinioninformationfromanunstructuredreviewtextisquitedifficult.Thisstudyaimstoclassifythesereviewsas"positive"or"negative".ThemodelproposedforclassificationisLSTM.LongShort-TermMemory(LSTM)wasusedinthepreviouslytrainedmodeltoclassifythisreview.Themodeldesignedforthemodel focuses on preprocessing reviews as follows: data cleansing, case folding, normalization,tokenization,stopword,andstemming.Onceclassified,thisreviewisvisualizedasagraph.Thebest-casescenarioresultswithanaccuracyof95,10%onthesentimenttowardsthepriceaspect.


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ABSTRACTSESSION5D:DataScience

IndonesianIDCardExtractorUsingOpticalCharacterRecognitionandNaturalLanguagePost-

Processing

FirhanMaulanaRusli,KevinAkbarAdhiguna,HendyIrawan

Thedevelopmentofinformationtechnologyhasbeenincreasinglychangingthemeansofinformationexchangeleadingtotheneedofdigitizingprintdocuments.Inthepresentera,thereisalotoffraudthatoftenoccur.Forexampleisaccountfraud,toavoidaccountfraudtherewasverificationusingIDcardextractionusingOCRandNLP.OpticalCharacterRecognition(OCR)istechnologythatusedtogenerate text from image. With OCR we can extract Indonesian ID card or \emph{kartu tandapenduduk} (KTP) into text using 3 different OCR library, PyOCR, Pytesseract, and TesseOCR. ToimprovedtheaccuracywemadetextcorrectionusingNaturallanguageProcessing(NLP)basictoolsto fixing the text. With 50 Indonesian ID card image we compared the performance with threedifferentOCRlibrary.TheresultofourexperimentshowthatPytesseracthadthebestperformancewith0.78F-scoreand4510millisecondstoextractperIDcard.

AnalysisofRecordsManagementMaturityLevelforDataCollectionofNetworkAssetsinIndonesianTelecommunicationIndustry

RizkyA.C.EkaPutri,AchmadHidayanto

PT.XYZisastate-ownedcompanyintelecommunicationsandinformationtechnology.Indevelopingitsbusiness,PT.XYZhasservedmorethan2.200corporatecustomersand22.760retailcustomersbyhavingalotofnetworkassets.Thelargenumberofnetworkassetsownedmeansthatthecompanymustbeabletocarryoutgooddatacollectiontofindoutinformationonthedistributionandcapacityof assets that are still available. Themost commonproblems encountered at PT.XYZ is about datarecordsmanagementofnetworkassets.PT.XYZalreadyhasasystemfordatacollectionofnetworkassets, but it's still not running well. This occurs because there are no procedures and clarity ofaccountabilityrelatedtorecordsmanagement,sothereweremanydatacollectionprocessesthatwerestillmanually.Basedontheproblem,thepurposeofthisstudyistoconductanassessmentofrecordsmanagement level at PT.XYZ and provide recommendations to increase the level. The assessmentprocessandrecommendationswillbebasedonARMAGARPFramework.Thiswaschosenbecauseit'sa comprehensive approach that widely used in managing the information assets of companies ororganizations.Theresultsofmaturitylevelassessmentofthisstudyareatlevel2foraccountability,transparency, compliance, availability, retention, and also level 1 for integrity, protection, anddisposition.TheseresultsalsoindicatethatthecompanyneedstoimproveseveralprocessesintheGAPRPrinciples of Integrity, Protection, andDisposition because there is a large gap between thecurrentlevelandtheexpectedtargetlevel.

DataAcquisitionGuideforForestFireRiskModellinginMalaysia

YeeJianChew,ShihYinOoi,YingHanPang

Availabilityofremotesensingdata(i.e.,informationcapturedfromsatellite)inconjunctionwiththeusageofGeographicInformationSystem(GIS)hasmadeitfeasibletodeliverafiremodelcapabletosegregatetheareaintoahigherorlowerriskfireregion.Theadvancementoftechnologieshasalsoinauguratedthepossibilityto incorporateremotesensinginformationandothergrounddata(e.g.,meteorological data, distance to road data, etc.) by utilizingmachine learning classifiers or deeplearningalgorithmtopredicttheforest fireoccurrence.However, itshouldbehighlightedthatthe


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dataacquisitionproceduremayvarydependingonthevicinityofthestudyareasincesomedataareonlyobtainablefromthespecificgovernmentauthority.Inthispaper,wewillbedisclosingsomeofthe publicly accessible remote sensing data and some of the valuable data attainable from theMalaysian government that is useful for detecting forest fire in Malaysia. Additionally, previousstudiesandworksthathaveemployedthedatasourcetomapforestfirearealsodeliberatedinthispaper.OnlythedatathathadbeenexploitedinthepastforMalaysiaarediscussed.

ImplementationofHiddenMarkovModel(HMM)toPredictFinancialMarketRegime

BambangWahyudi,IrmaPalupi,AgungPutraThisworkperformshowtoimplementtheconceptofHiddenMarkovModel(HMM)tofindfinancialmarkettrendforgivenonlytheobservedstateobtainedfromthestockprice.Theconsideredmarkettrendissetasahiddenstate, that inthe financial technicalanalysisknownasBearish,Bullish,andSideway,which are important for decisionmaking of stock trading in order to recognize the goodmomenttosell,tobuyortojustholdtheshares.InordertoobtainthemostlikelysequenceofhiddenstatesthroughHMM,whichiscomputationallycanbeadynamicprogrammingproblem,weexplainhow the Viterbi algorithm work for the case in this study. To get the stock price prediction asobservationstates,theARIMAmodelisusedbasedonexperimentaltrialoffittingmodel,thenusetheresultasapredictedobservedstatesthatbetheinputtopredictthemarkettrendusingHMMfortheshortperiodoffuturetime.Severalinterestingresultsofhiddenmarkettrendanditsstudyarealsoprovided,includingtheaccuracy,precision,recallandtheconsistencyofthemodeltothegivendataset.

PredictionofGraduationwithNaïveBayesAlgorithmandPrincipalComponentAnalysis(PCA)

onTimeSeriesData

WishnuDwiHerlambang,KusumaAyuLaksitowening,IbnuAsrorOn-timegraduationisanachievementforbothstudentsanduniversity.Foruniversity,especiallystudyprogram,thepercentageofgraduationon-timeisacrucialpointforaccreditation.Therefore,itrequiresagoodstrategy.Thepercentageofgraduationontimeincollegecanbepredictedwithdataminingandmachinelearning.Theobjectiveofthisresearchistoprovideearlierinformationregardingstudentswhoareatriskonnotgraduatingontime.Thus,thestudyprogramcantakeappropriateactionbeforeit is too late.Thereareseveralclassificationmethodsthatcanbeusedforprediction.OurresearchcombinesNaïveBayeswithPrincipalComponentAnalysis(PCA).PCAisusedtosimplifythecomplexacademic data. The PCA result that has simpler structure then to be processed usingNaive Bayesclassification.ThisresearchusesdataobtainedfromfourbatchofstudentacademicperformancedatainInformaticsStudyProgram,TelkomUniversity.Thedatasetispartitionedbyacademicyeartoobtaintimeseriesdataofeachstudent.ThecombinationofPCAandNaïveBayesalgorithmsobtainedbetterresultcomparedtoclassificationusingNaïveBayesonly,withinaverage3.65%higheraccuracy.


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ABSTRACTSESSION5E:DataScience

ComparativeAnalysisofSupportVectorMachine(SVM)andRandomForest(RF)Classification

forCancerDetectionusingMicroarray

Irawansyah,Adiwijaya,WidiAstuti

Cancer is the second leading cause of death globally.According to theWorldHealthOrganization(WHO)in2018,approximately9.6milliondeathswerecausedbycancer.Globally,about1in6deathsarecausedbycancer.Onewaytodetectcanceristousemicroarraydataclassification.Microarraytechnologyisusedtodetecttheexpressionofthousandsofgenesatthesametimetoanalyzeanddiagnose cancer. However, microarray data has high dimensions because of its large number offeaturesandlowdatadistribution,whichmeansthatithasasmallnumberofdatasamples,whichcauseslowperformance.Toovercomethisproblem,dimensionreductionisneeded.Therefore,itisnecessarytoreducethedimensionsofmicroarraydatawithRandomProjection(RP)toreducethehighdimensionsandusetheSupportVectorMachine(SVM)andRandomForest(RF)asclassificationmethods.TheclassificationmethodwillbecomparedandanalyzedtodeterminewhichclassificationmethodproducesthebestperformancebyusingRandomProjection(RP)asadimensionalreductionmethod.Basedonthesystemthathasbeenbuilt,thebestaccuracyforcolontumordatais69.23%withRandomProjection (RP)-SVM,LungCancer is 100% forbothmethods classification,OvarianCancer is 100% for bothmethods classification, the prostate tumor is 95.12% for bothmethodsclassificationand66.66%forbothmethodsclassification.EvaluatingtheBPPTMedicalSpeechCorpusforAnASRMedicalRecordTranscriptionSystem

ElviraNurfadhilah,AsrilJarin,LylaRuslanaAini,SiskaPebiana,AgungSantosa,MuhammadTeduh

Uliniansyah,EduwardButarbutar,Desiani,Gunarso

In joint research fundedbyLPDP,BPPT is collaboratingwithSolusi247andHarapanKitaNationalHeart and Vascular Hospital to develop a speech corpus named BPPTMedical Speech Corpus forbuildingamedicalASR(automaticspeechrecognition)system.Thecorpuswasproducedbyrecording100 speakers for 81.68 hours. The corpus has 600 unique sentences containing medical terms.Previously,BPPThasdevelopedaspeechcorpusforthegeneralnamedBPPTGeneralSpeechCorpus.AthirdspeechcorpusnamedBPPTCombinedSpeechCorpuswascreatedbycombiningthemedicalandgeneralcorpus,andeachcorpuswastrainedusingPyChaintobuildanASRmodel.Basedontheexperimentsweconducted,theASRmodelbuiltfromthecombinedspeechcorpushasthebestWERof6.10%.

ImplementationofSimulatedAnnealing-SupportVectorMachineonQSARStudyofIndenopyrazoleDerivativeasAnti-CancerAgent

MuhammadFajarRizqi,RezaRendianSeptiawan,IsmanKurniawan

Cancerisadiseasethatoccursduetotheuncontrolledgrowthofabnormalcellscausingbodytissuedamage.Thisdiseaseisconsideredasadeadlydisease.In2019,1700deathsoccureverydayduetocancer.Someeffectiveanticanceragentsareknowntocausetemporarytochronictoxiceffects.Thereare several compounds that have the potential to become anticancer drugs, one of them isindenopyrazole.Thegoalof thisresearch is to implementsimulatedannealingandsupportvectormachinemethodintheQSARstudytopredicttheactivityofindenopyrazolederivativesasanticancerdrugs.Simulatedannealingisusedforfeatureselectionandsupportvectormachineisusedformodeldevelopment.Inthisresearch,weusedthreekernelmodelsforSVM,namelySVMwithRBFkernel,


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SVMwithlinearkernel,andSVMwiththepolynomialkernel.Fromthreemodelsthatwereregressed,SVMwithRBFkernelhasparameterC=10,gamma=scaleandepsilon=0.1produceR2scoretrainandtest 0.79 and 0.60, respectively. SVM with linear kernel has parameter C=1000, degree=1 andepsilon=0.1produceR2scoretrainandtest0.61and0.63,respectively.SVMwithpolynomialkernelhas parameter C=1000, degree=2 and epsilon=0.1 produceR2 score train and test 0.72 and0.50,respectively.Basedonthevalidationresults,onlymodelwithRBFkernelwhichparameterssatisfyallthecriteria.FromtheresultwecanconcludethatthemodelwithRBFkernelisthebestmodelandacceptable.

RansomwareDetectiononBitcoinTransactionsUsingArtificialNeuralNetworkMethods

Hairil,NikenCahyani,HilalH.NuhaTheuseof digital currencyor cryptocurrency in various virtual transactions is commondue to itseasiness. Cryptocurrency is a digital currency that is used for virtual transactions on the internetnetwork.ThemostcommontypesofcryptocurrenciesincludeLitecoin,Ethereum,Monero,Ripple,andBitcoin.Eventhoughcryptocurrencieshavesecretcodesthatarequitecomplicatedandcomplexthatservetoprotectandmaintainthesecurityofdigitalcurrencies,itispossibletobehackedbyskilledhackers.Cryptocurrency-relatedhackingisatypeofdigitalcrimethatisveryharmfulanddangerous.Forexample,inrecentyears,casesofhackingonbitcointransactionsusingransomwarehavebeenontherise.Ransomwareismalicioussoftwarethatsecretlyinfectsavictim'sdevice,andsuddenlyasksforaransomtodecryptencrypteddata.Asthenameimplies,ransomwhichmeansransom,thistypeofmalwareaimstoblackmailavictimwhosecomputerisinfectedwithransomwarebyaskingforacertain amount ofmoney as a ransom.Therefore, a designwasbuilt in the formof a ransomwaredetection system based on available bitcoin heist data so as to minimize hacking attacks againstcryptocurrencyinthefuture.TheransomwaredetectionsystemwasbuiltusingthebackpropagationartificialneuralnetworkmethodusingWekasoftware.Thebestresultsindatatestingareusingtheparameternumberofhiddenlayerwith9neurons;learningrate0.1;andthenumberofiterationsof5000yieldsanaccuracyrateof97%.

EmotionalContextDetectiononConversationTextwithDeepLearningMethodUsingLong

Short-TermMemoryandAttentionNetworks

AfridaHelen,MiraSuryani,HidayatulFakhriTheconversationinthetextisaninterestingresearchonNaturalLanguageProcessing.Oneofthetextconversation tasks is to know the emotions of the people involved in the conversation. TheconversationinsocialmedialikeTwitter,Instagram,shortmessageservice,WhatsApp,andsoon,ofteninvolves emotion. Somehow the comments are impulsive sentences that can stimulate emotions.Expressing emotionsusing text is rarelydone anduncomfortable.However,withnatural languagetechnologydevelopment,expressingemotionsusingtextcanbesucceededwithspecificsymbols.Wecall thespecific symbolsemojis.Somanyemojis canexpressemotions.This researchproposes theemoji symbol as a character feature. We introduce the Emoji2Vec method and Long Short-termMemorywithAttention.TheAttentionthatisusedhasacomplextopology.Wecomparedtheresultsofthisstudywiththebaselinemodel.Themethodweproposeisbetterthanthebaselinemodel.


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Documents

ADVANCED PROGRAM · 2021. 8. 2. · ADVANCED PROGRAM | ICOICT 2021 3 COMMITTEE Steering Committee § A Adiwijaya, Telkom University, Indonesia § Ahmad Rafi, Multimedia University,