Editorial Team - Universitas Trunojoyo Madurateknik.trunojoyo.ac.id/ft_utm/images/Bain_Khusnul_Khotimah/Lampiran... · 3. Prof. Sarwoko ... Ardi Nugroho Yulianto, I Ketut Suastika,

Editorial Team

Editorial in Chief

1. Ms. Aulia siti Aisjah, Editor IPTEK Journal for Technology and Sciense

Members

1. Dr Setiyo Gunawan, Chief Editor IPTEK Journal of Engineering, Indonesia

2. Prof. Gamantyo Hendrantoro, Department of Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya,

Indonesia, Indonesia

3. Prof. Sarwoko Mangkoedihardjo, Inviromental Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia,

Indonesia

4. Mr. Mauridhi Hery Purnomo, Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia,

Indonesia

5. Mr Suhartono Suhartono, Chief Editor IPTEK Journal of Science, Indonesia

6. Prof. Imam Robandi, Electrical Engineering, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia, Indonesia

No 1 (2014) International Seminar on Applied Technology, Science, and Arts (APTECS) 2013

APTECS is an Annual International Seminar on Applied Technology, Science, and Arts. APTECS

has been held by LPPM-ITS since 2009. Starting from the year of 2013, the 4th APTECS, there will

be three main events, namely the International Conference, the Business Forum, and the Exhibition.

Table of Contents

Articles

Development of a ‘Fish Tail’ Rudder to Improve a Ship’s Maneuverability in Seaway

1-7

Aries Sulisetyono

Maximum Peak-Gain Margin 2DOF-IMC Tuning for a 2DOF-PID Filter Set Point

Controller Under Parametric Uncertainty

8-16

Nur Hidayah, Juwari Juwari, Renanto Handogo

Design of Curriculum Matrix for Robotics Education Derived from Bloom's

Taxonomy and Educational Curriculum of 2013

17-25

Eko Henfri Binugroho, Endah Suryawati Ningrum, Dwi Kurnia Basuki, Adnan

Rachmat Anom Besari

Viscous-Resistance Calculation and Verification of Remotely Operated Inspection

Submarine

26-30

Ardi Nugroho Yulianto, I Ketut Suastika, Aries Sulisetyono

Bioremoval of Chromium, Copper and Cadmium by Bacillus cereus in Simulated

Electroplating Wastewater

31-35

Yulinah Trihadiningrum

An Investigation into Hybrid Catamaran Fishing Vessel: Combination of Diesel

Engine, Sails and Solar Panels

36-41

Pramudya Imawan Santosa, I Ketut Arya Priya Utama

Synthesis of UiO-66 Using Solvothermal Method at High Temperature 42-46

Ika Diah Rahmawati, Ratna Ediati, Didik Prasetyoko

On-line State Estimator for Three Phase Distribution Networks Displayed on

Geographic Information System

47-50

Indri Suryawati, Ontoseno Penangsang, Adi Suprijanto, Dimas Fajar U. P.,

Mat Syai’in

Impacts of River Groove Propagation on Irrigation Infrastructural Failures 51-57

Kuntjoro Kuntjoro, Choirul Anwar, Didik Harijanto

Steering System Kinematic and Steady-State Cornering Analyses of the ITS Electric

Car

58-62

Unggul Wasiwitono, Indra Sidarta, Agus Sigit Pramono, Sutikno Sutikno, Alief

Wikarta

The Rate of Turbulent Kinetic Energy Dissipation in a Turbulent Boundary layer on

a Flat Plate

63-66

Sutardi Sutardi

The Influence of Burning Temperature of MIRHA On Mechanical Properties of

Foamed Concrete

67-72

Ridho Bayuaji

System Application of Genetic Algorithm for Scheduling Optimization Study Using 73-76

http://iptek.its.ac.id/index.php/jps/article/view/376








http://iptek.its.ac.id/index.php/jps/article/view/337/225























Java (Case Study : Department of Computer System UNTAN)

Fatma Agus Setyaningsih

Simulation of Direct Digital Synthesizer with LabView 77-81

Elan Djaelani Suardja

Technology Development of Kupang Processing with Dryer Machine Base on

Expert System

82-88

Dedin F Rosida, Dedid Cahya Happyanto

Kinetics of Hg and Pb Removal in Aqueous Solution Using Coal Fly Ash Adsorbent 89-92

Eko Prasetyo Kuncoro, Mochammad Zakki Fahmi

Microstructures Behavior in Biomedical Co-Cr-Mo-C-Si-Mn Alloys Containing

Nitrogen

93-96

Alfirano Alfirano

New Concept of PQR-Coordinate on Power Transformation for Harmonics

Reducing

97-101

Indriarto Yuniantoro, Rudy Setiabudy, Ridwan Gunawan

The Effect of High Switching Frequency on Inverter Against Measurements of

kWh-Meter

102-109

Isdawimah Isdawimah, Rudy Setiabudy, Ridwan Gunawan

Analysis of Buttering Method on Mechanical Properties Welded Material Low

Carbon Steel

109-113

Gathot Dwi Winarto, Rivai Wardhani, Irsyadus Syarif

Vibration and Cylinder Chamber Pressure Characteristics of an Air Reciprocating

Compressor

114-118

Bambang Daryanto Wonoyudo, David A. Siregar

The Effect of Chemical Substance and Immersion Time to Tensile Strength of

Bamboo Betung (Dendrocalamus asper) as Chemical Preservation Treatment

119-124

Afif Rizqi Fattah, Ken Ninez Nurpramesti Prinindya, Hosta Ardhyananta

Optimal Location of a Capacitor Bank in a Primary Feeder to Load up Linearly 125-129

Hermagasantos Zein, Erwin Dermawan

Power of Regenerative Braking with Kinetic Energy Recovery System 130-135

Mochamad Edoward Ramadhan, Harus Laksana Guntur

The Art and Sustainable Aspects of Natural Dyeing in KANAWIDA Hand Drawn

Batik (Green Batik)

136-143

July Hidayat, Fatmahwaty Fatmahwaty

Novel Hybrid Orthogonal Large Set Code Sequence for High Density Wireless

Networks

144-152

Nyoman Pramaita, P. L. Johnson

Novel Centralized Control Battery Operation, on Microgrids to Supply The Load

Priority

153-159

Hartono Budi Santoso, Wike Handini, Rudy Setiabudy, Budiyanto Budiyanto

Learning based on Virtual Class using combination of Second Life and Learning

Managment System

160-163

Firli Irhamni, Indah Agustien Siradjuddin, Arik Kurniawati, Ari

Kusumaningsih, Rima Triwahyuningrum



































Physic Simulation Experiments using Augmented Reality 164-166

Surya Sumpeno, Christyowidiasmoro Christyowidiasmoro

Performance of Anaerobic Reactor in Attached and Suspended Growth Mechanisms

for the Removal of Tofu Wastewater

167-168

Rosvita Tri Jullyanti, H. Purnobasuki, T. Nurhariyati, F. N. Rachman, N. I.

Oktavitri

Analysis Characteristic of Province Travelling Intercity of AKDP Makassar-

Parepare Route, South Sulawesi

169-173

St. Maryam Hafram, Herman Parung, Tri Harianto, Muh. Isran Ramli

Investigation of Flow and Heat Transfer Characteristics in Two-Phase Flow

Through Evaporator of Diffusion Absorption Refrigerator (DAR)

174-180

Siti Masita Rakhmawati

Detection of Critical Situation of Longitudinal Vehicle Dynamics 181-187

Widya Nila Velayati, Sebastien Varrier

A Robust Frequency Control Approach in PV-Diesel Hybrid Power System 188-194

Heri Suryoatmojo, Adi Kurniawan, Feby Agung Pamuji, Nursalim ST MT, IGP

Suta Wijaya, Herbert Innah

Assessment the Role of Sediment in the Sumber Brantas Rivers Watershed Affected

by Agroforestry Activities

195-200

Barlah Rumhayati, Hari Siswoyo, MH. Chusnuddin Nurochman

Study at the Bolted Joint Assembly with Slotted Hole 201-210

Ramadhan Prasetya Utama, Alain Daidié

Augmented Reality Prototype to Introduce Gamelan Based on Web 211-217

Avriyasendy Ramadiyan, Sari Wijayanti

Power Flow Control of Power Systems Using UPFC Based on Adaptive Neuro

Fuzzy

218-223

Agus Jamal, Ramadoni Syahputra

Distribution Network Efficiency Improvement Based on Fuzzy Multi-objective

Method

224-229

Ramadoni Syahputra, Imam Robandi, Mochamad Ashari

Crystal Growth of IRMOF-3 (Isoreticular Metal-Organic Frameworks-3)

Synthesized using Solvothermal Method

230-234

Pemta Tia Deka, Ratna Ediati, Didik Prasetyoko

Utilization of Fertilizers for Improving The Process Rate of Organic Material

Degradation in Anaerobic Reactor

235-238

Adelia Anju Asmara, Agoes Soegianto, Trisnadi Widyaleksono Catur Putranto,

Hairul Amin, Nur Indradewi Oktavitri

Database Integration Model for Automatic Identification System and Shipping

Database In Real Time Traffic Monitoring

239-244

Akhmad Maulidi, Trika Pitana, Ketut Buda Artana, A.A. Bagus Dinariyana,

Muhammad Badrus Zaman, Agoes A. Masroeri, Ricky Randall Sembiring

Energy Efficiency Analysis of Trawlers (Case Study: Indonesian’s Trawler) 245-250

Alyuan Dasira, Jean Marc Laurens

Analysis of The Effect of Linear and Non-Linear Loads against The Quality of

Power at The Power Converter

251-257








































Anicetus Damar Aji, Kusnadi Kusnadi

Interactive Whiteboard and Instant Quiz for Multimedia Classroom Activity 258-263

Ary Mazharuddin Shiddiqi, Royyana M. Ijtihadie, Emerson Eridiansyah Z,

Baskoro Adi Pratomo, Tohari Ahmad, Waskhito Wibisono

Chemical Bonds Visualization using Particle Effect and Augmented Reality 264-268

Christyowidiasmoro Christyowidiasmoro, Surya Sumpeno

The Alternative Use Of Water Hyacinth and Reed As Attached Growth Of

Microbial In Waste Water Treatment

269-271

Hery Purnobasuki, Nur Indradewi Oktavitri, Tri Nurhariyati, B. Saadah,

Santini Ika Rafsanjani

Modelling and Analysis of the Dynamic Behavior of a Double Cylinder Inline 650cc

Gasoline Engine with Crank Angle 0o for Rubber Mount

272-278

Harus Laksana Guntur, Karina Yulia

The Erythemato-Squamous Dermatology Diseases Severity Determination using

Self-Organizing Map

279-284

Haryanto Haryanto, Miftahul Ulum, Diana Rahmawati Rahmawati, Koko Joni,

Ahmad Ubaidillah, Riza Alfita, Lilik Anifah, Bain Khusnul Khotimah

Location Time of Power Quality Disturbances and Noise Using Morphology

Gradient and Skeletonization in 3D

285-290

I Gusti Ngurah Agung Dwijaya Saputra, Henry Wu, Wenhu Tang

Pattern of Biogas Production and Removal of Chemical Oxygen Demand in Semi

Continues Hybrid Anaerobic Reactor

291-293

Nur Indradewi Oktavitri, Agoes Soegianto, T. Rahman, Bidayatus Saadah,

Deavy Trianingtyas

Three-Dimensional (3D) Reconstruction for Detecting Shape and Volume of Lung

Cancer Nodules

294-298

Rodiah Rodiah, Sarifuddin Madenda, Fitrianingsih Fitrianingsih

Thermal Performance of Apartment (High-Rise) in Surabaya with Precast Concrete

for the Building Envelope

299-305

Romanria Violina Mahardhika, Ima Defiana, V. Totok Noerwasito

Pretreatment of Sludge Milk Waste as Source Of Composting using Microbes 306-310

S. R. Juliastuti, Rian Setya Budi, Taufiqurrusydi Taufiqurrusydi

Further Investigation on Building and Benchmarking A Low Power Embedded

Cluster for Education

311-318

Sritrusta Sukaridhoto, Achmad Subhan Khalilullah, Dadet Pramadihanto

Real Time Train Monitoring using Google Maps 319-322

Supeno Djanali, Ary M. Shiddiqi, Hudan Studiawan, Henning T. Ciptaningtyas

Structural Damage Detection of A Steel-Truss Railway Bridge Using its Dynamic

Characteristics

323-329

T. Susanto, A. Budipriyanto

Android App for Information of Food Additives Contained in Packaged Foods 330-336

Umi Sa'adah, Tita Karlita, Muchammad Arfian



































IPTEK, Journal of Proceeding Series, Vol. 1, 2014 (eISSN: 2354-6026) 279

The Erythemato-Squamous Dermatology

Diseases Severity Determination using

Self-Organizing Map

Haryanto1, Bain Khusnul Khotimah

2, Miftahul Ulum

1, Diana Rahmawati

1, Koko Joni

1,

Ahmad Ubaidillah1, Riza Alfita

1, and Lilik Anifah

1

AbstractA new approach based on the implementation of Self Organizing Map is presented for automated detection of

erythemato-squamous diseases. The purpose of clustering techniques is in order to determinate the severity of erythemato-

squamous dermatology diseases. The studied domain contained records of patients with known diagnosis. Self-Organizing Map

algorithm's task was to classify the data points, in this case the patients with attribute data, to one of the six clusters (psoriasis,

seboreic dermatitis, lichen planus, pityriasis rosea, cronic dermatitis, dan pityriasis rubra pilaris). The algorithm was used to

detect the six erythemato-squamous diseases when 33 features defining five disease indications were used. The purpose is to

determine an optimum classification scheme for this problem. The present research demonstrated that the features well

represent the erythemato-squamous diseases and SOM algorithm's task achieved high classification accuracies. The best

accuration for psoriasis 85,94%, seboreic dermatitis 40,48%, lichen planus 56,25%, and pityriasis rosea 82,61%, with learning

rate value were 0,1, 0,2, 0,9, and 0,4.

Keywords Erythemato-squamous, Self-Organizing Map, classification, dermatology.

I. INTRODUCTION1

he differential diagnosis of erythemato-squamous

diseases is a real problem in dermatology. They all

share the clinical features of erythema and scaling, with

very little differences. The diseases in this group are

psoriasis, seboreic dermatitis, lichen planus, pityriasis

rosea, cronic dermatitis, and pityriasis rubra pilaris.

Usually a biopsy is necessary for the diagnosis but

unfortunately these diseases share many

histopathological features as well [1].

Research about erythemato-squamous diseases has

been researched many researcher in the world.

Automatic detection of erythemato-squamous diseases

using k-means clustering has been investigated by

Ubeyli [2]. Classification of erythemato-squamous

diseases using Support Vector Machine investigated by

Derya [3] and another investigated by Loris [4].

Self-Organizing Map (SOM) invented by Kohonen [5],

[6]. SOM network learning some grop pattern and a

pattern made into a single cluster that has certain

characteristics [7]. SOM procedure is divided into three

parts, namely: Normalization, Trainings, and retrieval of

information from the training. The goal of normalization

is to avoid variable that has value too high against the

value of other data. Normalization process variable value

seeing a value between 0 and 1, is expected to be

detraining variable is normal with other data. After data

normalization, data matrix detraining shaped by a

particular procedure, recommended training held at least

500 times. The third step, namely the use trainings

results in the form of weights (weight) for each cluster.

Weight (weight) obtained is used to test the data that will

be tested.

1Haryanto, Miftahul Ulum, Diana Rahmawati, Koko Joni, Ahmad

Ubaidillah, Riza Alfita, and Lilik Anifah are with Departement of

Electrical Engineering, Trunojoyo University Madura, Bangkalan,

Indonesia. E-mail: [email protected];;;;;;. 2Bain Khusnul Khotimah are with Departement of Informatics

Engineering, Trunojoyo University Madura, Bangkalan, Indonesia.E-

mail: [email protected].

In this paper we present the application of a self-

organizing map to the problem of differential diagnosis

of erythemato-squamous diseases, which represents a

major issue in dermatology.

This paper is outlined as follows: In section 2 we

briefly describe material and the proposed method.

Experimental results and discussions will be presented in

section 3, followed by discussions in section 4,

conclusions in section 5, and future work in section 6.

II. MATERIAL AND METHOD

A. Material

We observed 231 erythemato-squamous diseases data

obtained from Machine Learning Repository (UCI)

dataset [1], included 30 training data and 201 testing

data. Training data contain 5 data for psoriasis, 5 data for

seboreic dermatitis, 5 data for lichen planus, 5 data for

pityriasis rosea, 5 data for cronic dermatitis, and 5 data

for pityriasis rubra pilaris.

The dataset constructed for this domain, the family

history feature has the value 1 if any of these diseases

has been observed in the family, and 0 otherwise. The

age feature simply represents the age of the patient.

Every other feature (clinical and histopathological) was

given a degree in the range of 0 to 3.

Hardware specification used in this research is Dell

Inspiron N3010 with Processor Intel(R) Core(TM) i5

CPU M430 @2,27 GHz installed memory (RAM) 2,00

GB.

B. Method

Method of Self Organizing Map (SOM) has been

progressing rapidly both in the substance of the SOM

and its application to clustering in various fields.

Modular network SOM developed by Kazuhiro

Tokunaga and Tetsuo Furukawa [9]. Networks Artificial

Counterpropagation developed by Davide Ballabio,

Viviana Consonni, and Roberto Todeschini [10]. SOM

of SOMs developed by Tetsuo Furukawa [11], Dieter

Müller developed a self-organized mapping of clusters of

T

mailto:[email protected]

280 IPTEK, Journal of Proceeding Series, Vol. 1, 2014 (eISSN: 2354-6026)

data into groups of neurons [12]. Comparision between

SOM-based optimization and particle swarm

optimization for minimized construction time from a

secant pile wall is researched by Jieh-Haur Chen [13].

Still a lot of research about other SOM. Figure of classic

SOM configuration is figured in fig 1.

Erythemato-squamous diseases classification in this

research using classic SOM. This research is divided into

9 experiments described in Table 1.

The clasters of this research are divided into 6 clasters

described in Table 2. Clasters of this research are

Psoriasis, Seboreic dermatitis, Lichen planus, Pityriasis

rosea, Cronic dermatitis and Pityriasis rubra pilaris.

Algorithm of this research is figured in fig 2. The step

of learning using SOM are initialization, determination

the number of classes, setting a learning rate parameter,

determination of the iteration value, and calculating the

distance between random data into each input weight. The classic SOM network learning algorithm can be

formulated as follows [6]: a. Set learning iteration number t = 0. Then initialize all

weights wij with small random values (or initialize

the weights using input data). Set the initial

topological neighborhood (d0). Set the initial learning

rate (0) and set the total number of iterations (T)

b. While iteration number (t) is less than T, repeat Step

3–Step 6.

c. Choose an input vector x randomly in the training set.

d. Determine the neuron j so that its weight vector wj is

the closest to the input vector and call it as the winner

neuron. The winner neuron j has the closest distance,

Dmin(t) to the input pattern x(t), where Dmin(t) is

given:

e. 2

min ))()((min)(min)( twtxtDtDj

ijiji ∑ −==

f. Update the weight vectors of both the winner neuron

j and its neighbors as:

g.

i

iii

Ni

tWxttWtW

∈∀−•+=+ ))(()()()1( α

h. where α(t) is a learning rate function that

exponentially decreases with time. Further, we define a neighborhood order function d(t) which

exponentially decreases with time. In this paper, the

following equations are used for α(t) and d(t):

i. [ ]Tt

Tt

edtd

et

3/

0

3/

0

)(

)(

−

−

=

=αα

j. where [y] denotes the largest integer less or equal to

y.

k. Set t = t+ 1 and if t < T go to Step 3, and otherwise

STOP.

III. RESULT AND DISCUSSION

Difficulty for the differential diagnosis is that a disease

may show the features of another disease at the

beginning stage and may have the characteristic features

at the following stages.

Patients were first evaluated clinically with 12 features.

Afterwards, skin samples were taken for the evaluation

of 22 histopathological features. The values of the

histopathological features are determined by an analysis

of the samples under a microscope. The Clinical

Attributes areerythema, scaling, definite borders, itching,

koebner phenomenon, polygonal papules, follicular

papules, oral mucosal involvement, knee and elbow

involvement, scalp involvement, family history, and

Age. Histopathological Attributes melanin incontinence,

eosinophils in the infiltrate, PNL infiltrate, fibrosis of the

papillary dermis, exocytosis, acanthosis, hyperkeratosis,

parakeratosis, clubbing of the rete ridges, elongation of

the rete ridges, thinning of the suprapapillary epidermis,

spongiform pustule, munro microabcess, focal

hypergranulosis, disappearance of the granular layer,

vacuolisation and damage of basal layer, spongiosis,

saw-tooth appearance of retes, follicular horn plug,

perifollicular parakeratosis, inflammatory monoluclear

inflitrate, and band-like infiltrate.

Parameters of learning are described in Table 1 for

minimal value, Table 2 for maximal value and Table 3

for average value. Table 3, 4, and 5 shows that the some

parameters had the overlap value among clasters and it is

not linear. That is why we try to overcome to classify

automatically using SOM.

Accuracy of each group is shown in fig 4, fig 5, fig 6,

fig 7, fig 8, and fig 9. Fig 4 is figured the accuray of the

fisrt group (Psoriasis), fig 5 is figured the accuray of the

second group (Seboreic dermatitis), fig 6 is figured the

accuray of the second group (Lichen planus dermatitis),

fig 7 is figured the accuray of the second group

(Pityriasis rosea), fig 8 is figured the accuray of the

second group ( Cronic dermatitis) and fig 9 is figured the

accuray of the second group ( Pityriasis rubra pilaris).

Fig 4 shows that the accuracy of cluster 1 (Psoriasis

group), first experiment and second experiment are

85,9375%. Thirth, fourth, and fifth experiment have

accuracy 84,375 % but sixth, seventh, eighth and ninth

experiment give accuracy 0%.

Fig 5 shows that the accuracy of second cluster

(Seboreic dermatitis group), first experiment and second

experiment are 40,476%, thirth has accuracy 0%, fourth

and sixth 21,428%, fifth 40,476, seventh and ninth have

accuracy 35,714%, and eighth experiment has accuracy

38,095%.

Fig 6 shows that the accuracy of thirth cluster (Lichen

planus group), first and second experiment are 31,25%,

thirth has accuracy 18,75%, fourth, fifth and sixth

12,5%, seventh 0%, eighth has accuracy 18,75% and

ninth experiment has accuracy 56,25%.

Fig 7 shows that the accuracy of fourth cluster

(Pityriasis rosea group), first experiment and second

experiment are 78,26%. Thirth is 69,565%, fourth has

accuracy 82,61%,fifth experiment has accuracy 4,34 %,

sixth has accuracy 60,869%, eighth has 17,39% but

seventh and ninth 0%.

Fig 8 shows that the accuracy of fifth cluster (Cronic

dermatitis group), first experiment and second

experiment are 86,49%. Thirth is 43,24%, fourth has

accuracy 35,13%, fifth experiment has accuracy 13,51%,

sixth has accuracy 37,84%, seventh and eighth have

48,65% but ninth has accuracy 10,81%.

Fig 9 shows that the accuracy of sixth cluster

(Pityriasis rubra pilaris group), first, second, thirth,

fourth, and seventh experiment 93,33%, fifth has

accuracy 7,14%, sixth experiment has accuracy 6,67%,

eighth has accuracy 100%, and ninth has accuracy 0%.

Time needs to process of each experiment is described

in Table 7. First, seventh, eighth, and ninth experiment


need 0,2028 second to process. Second and fourth

experiment need 0,187 second, thirth experiment needs

0,218 second, fifth and sixth experiments consume 0,265

and 0,234 second. Graph of time-consuming for each

experiment is figured in fig 10.

IV. CONCLUSION

In this research, we proposed an effective Erythemato-

squamous diseases classification system without the need

for user interaction. Experimental results have been

provided to show the successful of the system that could

help medical doctors as decision support system to

determine the claster of erythemato-squamous diseases.

A. Future Work

The system could classify the six claster (Psoriasis,

Seboreic dermatitis, Lichen planus, Pityriasis rosea,

Cronic dermatitis and Pityriasis rubra pilaris), the future

work we develop the more effective and robust

classification machine.

ACKNOWLEDGEMENT

This research was supported by UC Irvine Machine

Learning Repository (UCI).

REFERENCES

[1]. http://archive.ics.uci.edu/ml/

[2]. Ubeyli, Elif Derya and Erdoğan Doğdu 2008, Automatic detection of erythemato-squamous diseases using k-means

clustering has been investigated Journal of Mecidal System

Volume 34, Number 2, 179-184, DOI: 10.1007/s10916-008-

9229-6

[3]. Kohonen, T., 1982. Self-organized formation of topologically

correct feature maps. Biological Cybernetics 43, 59e69.

[4]. Kohonen, T., 1982. Analysis of a simple self-organizing process.

Biological Cybernetics 44, 135e140.

[5]. Kohonen, T., 2001. Self-Organizing Maps. Springer-Verlag,

Berlin.

[6]. A.M. Kalteh, 2008, Review of the self-organizing map (SOM)

approach in water resources: Analysis, modelling and

application, Environmental Modelling & Software 23 (2008)

835e845, Science Direct Elsevier.

[7]. Haykin, S., 1999, Neural Networks: A Comprehensive

Foundation, Prentice Hall Upper Saddle River, New Jersey.

[8]. Vesanto, J., Alhoniemi, E., 2000, Clustering of the self-

organizing map. IEEE, Transactions on Neural Networks 11 (3),

586e600. Woolf AD, Pfleger B. “Burden of major musculoskeletal conditions”, Bull World Health Organ

2003;81:646-656.

[9]. Kazuhiro Tokunaga, Tetsuo Furukawa, Modular network SOM,

Neural Networks 22 (2009) 82-90, Science Direct Elsevier,

2009.

[10]. Davide Ballabio, Viviana Consonni, Roberto Todeschini, The

Kohonen and CP-ANN toolbox: A collection of MATLAB

modules for Self Organizing Maps and Counterpropagation

Artificial Neural Networks, Chemometrics and Intelligent Laboratory Systems xxx (2009) xxx–xxx, Science Direct

Elsevier, 2009.

[11]. Tetsuo Furukawa, SOM of SOMs, Neural Networks 22 (2009)

463§478, Science Direct Elsevier, 2009.

[12]. Dieter Müller, Self organized mapping of data clusters to neuron

groups, Neural Networks 22 (2009) 415-424, Science Direct

Elsevier, 2009.

[13]. Jieh-Haur Chen, Comparison of SOM-based optimization and

particle swarm optimization for minimizing the construction

time of a secant pile wall, Automation in Construction 18 (2009)

844–848, Science Direct Elsevier, 2009.

Figure 1. Configuration of SOM

Figure 2. Algorithm of classic SOM

Figure 3. System accuracy using learning rate value 0,1 to 0,9

Figure 4. System accuracy using learning rate value 0,1 to 0,9 of cluster

1 (Psoriasis group)

Figure 5. System accuracy using learning rate value 0, 1 to 0, 9

of cluster 2 (Seboreic dermatitis group)

0

50

100

1 2 3 4 5 6 7 8 9

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9

0

10

20

30

40

50

1 2 3 4 5 6 7 8 9

Start

Number of Iteration

Choose input vector

t=number of iteration

End

Find the minimum distance

Weight update

Y

N



of cluster 4 (Pityriasis rosea group)


of cluster 5 (Cronic dermatitis group)


of cluster 6 (Pityriasis rubra pilaris group)

Figure 10. Time-consuming for each experiment

Figure 11. Comparison of each experiment accuracy

TABLE 1.

EXPERIMENTS OF THE RESEARCH

No Experiment Learning

rate Iteration

1 Experiment 1 0,1 500









TABLE 2.

CLASTER OF THE RESEARCH

No Claster Group

1 Claster 1 Psoriasis

2 Claster 2 Seboreic Dermatitis

3 Claster 3 Lichen Planus

4 Claster 4 Pitriasis Rosea

5 Claster 5 Cronic Dermatitits

6 Claster 6 Pityriasis Rubra Pilaris

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9

0

50

100

1 2 3 4 5 6 7 8 9

0

50

100

150

1 2 3 4 5 6 7 8 9

0

0.1

0.2

0.3

0 5 10

0

20

40

60

80

100

120

Claster 1 Claster 2 Claster 3

Claster 4 Claster 5 Claster 6


TABLE 3.

MINIMAL VALUE OF LEARNING PARAMETERS

Parameter 1 2 3 4 5 6

1: Erythema 0 1 0 1 0 1

2: Scaling 1 1 0 1 0 1

3: Definite borders 1 0 0 0 0 0

4: Itching 0 0 0 0 0 0

5: Koebner phenomenon 0 0 0 0 0 0

6: Polygonal papules 0 0 0 0 0 0

7: Follicular papules 0 0 0 0 0 1

8: Oral mucosal involvement 0 0 0 0 0 0

9: Knee and elbow involvement 0 0 0 0 0 0

10: Scalp involvement 0 0 0 0 0 0

11: Family history, (0 or 1) 0 0 0 0 0 0

12: Melanin incontinence 0 0 0 0 0 0

13: Eosinophils in the infiltrate 0 0 0 0 0 0

14: PNL infiltrate 0 0 0 0 0 0

15: Fibrosis of the papillary

dermis 0 0 0 0 1 0

16: Exocytosis 0 0 0 0 0 0

17: Acanthosis 0 0 0 0 1 1

18: Hyperkeratosis 0 0 0 0 0 0

19: Parakeratosis 0 0 0 0 0 0

20: Clubbing of the ridges 0 0 0 0 0 0

21: Elongation of the rete ridges 1 0 0 0 0 0

22: Thinning of the suprapaillary

Epidermis 0 0 0 0 0 0

23: Spongiformpustule 0 0 0 0 0 0

24: Munro microabcess 0 0 0 0 0 0

25: Focal hypergranulosis 0 0 0 0 0 0

26: Disappearance of the granular

Layer 0 0 0 0 0 0

27: Vacuolisation and damage of

Basal layer 0 0 0 0 0 0

28: Spongiosis 0 0 0 0 0 0

29: Saw-tooth appearance of retes 0 0 0 0 0 0

30: Follicular horn plug 0 0 0 0 0 0

31: Perifollicular parakeratos is 0 0 0 1 0 1

32: Inflammatory monoluclear

Infiltrate 0 0 0 0 0 0

33: Band-like infiltrate 0 0 0 0 0 0

34: Age (linear) 8 10 16 12 8 7

TABLE 4.

MAXIMAL VALUE OF LEARNING PARAMETERS


1: Erythema 3 3 3 3 3 3

2: Scaling 3 3 3 2 3 2


4: Itching 3 3 3 3 3 2











15: Fibrosis of the papillary dermis 0 0 2 0 3 0

16: Exocytosis 2 3 3 3 2 3

17: Acanthosis 3 3 3 2 3 2











Layer 3 0 2 2 0 0



28: Spongiosis 0 3 3 3 2 3







34: Age (linear) 75 75 75 75 75 75


TABLE 5.

AVERAGE VALUE OF LEARNING PARAMETERS


1: Erythema 3 3 3 3 3 3

2: Scaling 3 3 3 2 3 2


4: Itching 3 3 3 3 3 2











15: Fibrosis of the papillary

dermis 0 0 2 0 3 3

16: Exocytosis 2 3 3 3 2 3

17: Acanthosis 3 3 3 2 3 3











Layer 3 2 2 2 0 0



28: Spongiosis 0 3 3 3 2 3







34: Age (linear)

7

5

7

0

6

5

7

0

7

0

5

6

TABLE 6.

ACCURACY OF WHOLE SYSTEM

No Experiment Accuracy (%)

1 Experiment 1 (with learning rate 0,1) 70,149









TABLE 7.

TIME-CONSUMING FOR EACH EXPERIMENT

Learning Rate Time (Second)

Experiment 1 (learning Rate 0,1) 0,2028013









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