MEASUREMENT AND STATISTIC LABORATORY - industri…industri.usu.ac.id/images/Image/lab/MODUL-FULL.pdf · PRESENTATION MINIMUM STANDARD 1. Describes definitions & types of data 2. Describes

MEASUREMENT AND STATISTIC LABORATORY

INDUSTRIAL ENGINEERING DEPARTMENT - USU

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Acknowledgement

First of all, we want to express our thanks to Almighty God, because of His bless

and grace, and for the strength and abilities that He give to us, so the Guide Book of

Measurement and Statistic Laboratory can be finished on time.

This Guide Book will provide guidance and other information about the

implementation of the Measurement and Statistic Practicum to the participants.

This Guide Book is structured as a form of collaboration among the lecturers, staff,

and assistants of Measurement and Statistic Laboratory. Keeping track of the literature on

the modules to be practiced.

In the preparation, many sides have provided material and moral assistance. For

that we would like to say big thanks. There is nothing perfect, nor in the writing of this Guide

Book. For the limitations and imperfection of this guide book the authors expect the advice

and opinions of the readers and all practicipants for continuous improvement on the quality

of the activities of participants to be more applicable.

Medan, September 2017

Head of Measurement and Statistic Laboratory

Ir. Khawarita Siregar, MT.

NIP : 19591201 198601 2 001



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DISTRIBUTION TEST

Hypothesis testing is performed to determine wheter an expected frequency set is

equal to the frequency obtainned from distribution.

MULTI CRITERIA DECISION MAKING

AND CUSTOMER SATISFACTION RESEARCH

The research used to see how far the level of customer satisfaction of a

product/service by using assessment instrument in the form of questionnaire.

Experimental

Design

Experimental Design is the process of planning a study to meet specified objectives.

QUEUING

THEORY

Queue is an event occurence in daily life. Queueing theory is the mathematical

study of waiting lines, or ueues.

MODELLING

SYSTEM

Modelling System can help us to learn and understand the complex system in a

relatively short time.

QUALITY CONTROL WITH

SIG SIGMA APPROACH

Six Sigma is defined as a sophisticated technological tool used by statisticians to

improve or develop a rocess or product.

A B

C

A

D

E

F



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A. PRECONDITION

1. Administration registered in Industrial Engineering Department Faculty Engineering of USU, that

can be proved by the card of study planning and and card of study results in the connected

semester.

2. Have been taking and finished Industrial Statistic subject with minimal C.

3. Have been taking and finished Probability Theory subject with minimal C.

4. Have been listed Statistics Lab in the card of study planning.

5. Have been taking experimental design subject.

6. Pass on the pre-test.

B. REGULATION AND DISCIPLINE OF PRACTICUM

1. a. .Briefing and betting activities are

included in the practicum activities, if

the participans :

1) late up to 10 minutes from the

appointed time the participants

will be recorded into blackbook.

2) Late more than 10 minutes, will be

given Surat Peringatan (SP).


forbidden to attend practicum

and declared failed.

b. Practicum will be done at the specified

time, if the participants:

1) late up to 10 minutes, will get -10 of

his/her slide score.


given SP.

3) Late to 30 minutes, will be

forbidden to follow practicum and

declared failed.

2. Participants must be attend all

practicum’s activity, if the participants are

not attending (absent) without any notice

and unclear reason, he/she will be

forbidden follow any practicum’s activity

and be declared failed and be given

penalty sanction to not participate for

batch. If the participants are unable to

attend due to the illness then he/she have

to give an illnes letter to the Measurement

and Statistic Laboratory and submit it

before the time of practicum and will be

given SP.

3. Before conduct the laboratory, the

participants must bring:

a. Presentation slide and assignment

have been completed. If the

participants aren’t completed them,

then the participants will be given SP.

b. The badgename and module. If the

participants not bring the badgename

or the module, then the participants

will be given SP.

c. Study of literature that connected with

the module that be practiced, if the

participants didn’t bring the study of

literature, will be recorded in the

blackbook. If the participants has

been listed on the blackbook 3 times

accumulatively, the participants will

be given SP.



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4. Each participants must be well-dressed,

(t-shirt, socks, sneakers). If the participants

aren’t follow the instruction, will be given

up to 10 minutes to completed the

instruction, and if more than 10 minutes will

be given SP. If more than 30 minutes, the

participants will be forbidden to follow the

practicum and be declared failed.

5. During the practicum the participants are

forbidden to:

a. Leave the practicum without the

assistant’s permission.

b. Smoking in Measurement and Statistics

Laboratory area.

c. Eat and drink ( etc mineral water) .

d. Use the phone during the practicum.

e. Make a commotion in the

laboratorium. While practicum

(everything that makes the activities of

practicum not conducive).

NB : if the participants break the terms

of points a and b, then the participants

will be forbidden to follow the

practicum’s activity and be declared

failed, whereas if the participants break

points c,d and e then the participants

will be recorded into blackbook.

6. After conduct the practicum, each group

must be:

a. Write all results of practicum and give

the report according to assistant’s

instruction.

b. Cleaning and tidying the laboratory

and return the used equipment.

7. Equipment damaged caused by the

participants must be replaced by the

participant’s group in the time specified.

8. Each participants must be attend at

assistance activity, if the participants are

not attending the activity, he/she must be

given clear reason to the assistant. If the

participants didn’t give reason, the

participants will be recorded in the

blackbook.

9. The participants must peform assistance at

least 5 times in each report. If less than 5

times, the group will be given a sanctions

reduction of a value -10 per number of

such assistance deficiencies.

10. If the participants don’ get ACC, don’t

collect the internet journal, and the report

at the appointed time, then the

participants will be given SP.

11. For participants who not collect the report

at the appointed time, the group will be

given time to finished and collect the

report 1 day next. If passed by the time

limit that has been specified, then the

participants will be declared failed.

12. If the participants have 2 SP comulatively,

then the participants are forbidden to

follow the practicum and be decraded

failed.

13. The activity of assistace is only permitted in

the Measurement and Statistics

Laboratory room and is limited until 5.00

pm.

14. The participant must be made slide

presentation by his/her self, if there are

same slide or plagiat, the participant will

be recorded in blackbook in 2 times.

15. If the participants are 2 times cumulatively

not doing cleanliness duty, then the



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participants will be recorded in the

balckbook.

16. Each decision we make is the result of the

compound among assistant, head and

staff of laboratory.

C. MANAGEMENT PERSONNEL

1. Head of Laboratory : Ir. Khawarita Siregar, M.T.

2. Staf of Laboratory : Aulia Ishak, S.T., M.T.

Ir. Elisabeth Ginting, M.Si.

Khalida Syahputri, S.T., M.T.

3. Assistant of Laboratory :

DEMISIONER

Liwanto Muhammad Gabriel Haura Amany Abdi

Jean Ayuningthias Siti Khairunnisa Br Bangun Sri Litna Br Perangin-angin



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TOP MANAGEMENT

Hansen Janustra Naibaho

Assistant Coordinator Kevin Fan

Program Coordinator

Hp. [081264060267] [[email protected]]

Hp. [085272772559] [[email protected]]

Vina Akmaliah Secretary

Syafiah Khairunnisa

Treasurer, Public Relations & IT and Cleanliness Coordinator

Trie Dinda Maharani P

Library, Equipment and Development Coordinator

Hp. [082164242097]

[[email protected]]

Hp. [082232417971]

[[email protected]]

Hp. [081394648379]

[[email protected]]



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Assistant

Winston

Member Library M. Faisal Ardiansyah

Member Equipment Tommy Wijaya

Member Equipment

081322251735 [email protected]



Inggrid Marcelina MS Member Library

Feby Sana Sibarani

Member ISO Nurul Hidayati

Member Cleanliness






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D. ASSESSMENT OF PRACTICAL RESULTS

Practical Test = 20%

Pre Test = 50 %

Post Test = 50 % Total = 100 %

Module = 70 %

Debriefing = 35 %

Presentation = 20 %

Assistance = 15 %

Final report = 30 % Total = 100 %

Competition = 10 % Total = 100 %

Value A = 75.01 – 100 Value B+ = 70.01 – 75.00 Value B = 65.01 – 70.00 Value C+ = 60.01 – 65.01 Value C = 55.01 – 60.00 Value D = 45.01 – 55.00 Value E = 00.00 – 45.00

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INTRODUCTION

Testing a compatibility

hypothesis is a hypothesis test to

determine whether a given set

of frequencies is equal to the

frequency obtained from a

distribution, such as a bino mial,

poisson, normal distribution, or

from another comparison.

Thus, the test of goodness

of fit is a test of suitability or

goodness between observation

results (observation frequency)

certain with the frequency

obtained based on the

expected value (theoretical

frequency).

In this study, Chi-Square was

used to test whether the

observed frequencies deviated

significantly from an expected

frequency distribution. This test

will produce Chi-Square value

which will be compared with

Chi-Square value in table.

Furthermore, P-Value testing is

performed to determine the

extent to which the null

hypothesis is accepted.

PRACTICAL

PROCEDURES

1. In the discrete

distribution, experiments

are performed

according to the type of

distribution and recorded

into the observation

worksheet.

2. In a continuous

distribution, look for some

data coming from the

Badan Pusat Statistik

(BPS).

3. Testing data according

to its distribution by using

Chi-Square Test.

MODULE A

DISTRIBUTION TEST

DISCRETE DISTRIBUTION

&

CONTINUOUS DISTRIBUTION

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PRACTICUM GOALS

1. Be able to obtain discrete distribution data with direct experiment and continuous

distribution data from Badan Pusat Statistik (BPS).

2. Be able to understand and distinguish the types of discrete and continuous distribution

3. Be able to test the distribution of both discrete distribution and continuous distribution by

using Chi-Square Test.

4. Be able to understand the application and or application of distribution testing.

5. Be able to understand the characteristics of each distribution in accordance with the

data pattern.

PRESENTATION MINIMUM STANDARD

1. Describes definitions & types of data

2. Describes statistical methods.

3. Describe about hypothesis testing.

4. Describe the difference between discrete and continuous distributions.

5. Describe the discrete distribution and its types

6. Describe a continuous distribution and its types.

7. Describe the usefulness and application of distribution test

REFERENCES

1. Harinaldi. 2005. Prinsip-prinsip Statistik

untuk Teknik dan Sains. Jakarta: Penerbit

Erlangga.

2. Montgomery D.C. dan George C

Runger. Applied Statistic and Probability

For Engineer Fifth Edition. New Jersey:

John Wiley & Sons.

3. Spiegel, Murray R dan Larry J. Stephens.

2007. Schaum’s Outlines Statistik Edisi

Ketiga. Jakarta: Penerbit Erlangga.

4. Sugiyono. 2006. Statistika untuk

Penelitian. Bandung: CV Alfabeta.

5. Supranto, J. 2006. Statistik: Teori &

Aplikasi. Jakarta: Penerbit Erlanggga.

6. Walpole, Ronald E. 1982. Pengantar

Statistika. Jakarta: PT Gramedia Pustaka

Utama.

7. Walpole, Ronald E. 1995. Ilmu Peluang

dan Statistika untuk Insinyur dan

Ilmuwan. Bandung: Penerbit ITB.

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PRELIMINARY ASSIGNMENT

1. In the dice throwing experiment 180 times the following results were obtained.

x 1 2 3 4 5 6

f 28 36 36 30 27 23

Is the dice balanced? Note : Solve it using Goodness of Fit Test (α = 0,01)

2. In an experiment to examine the hypertension with smoking habits, the data obtained

from 180 people as follows:

Not

Smoker

Medium

Smoker

Heavy

Smoker

Hypertension 21 36 30

No Hypertension 48 26 19

Test the hypothesis that hypertension disease depends on smoking habits. (α = 0,05).

3. Maximum data = 200

Minimum data = last 3 digits of your NIM

Number of data (N) = 150

Determine the value of R (range), K (class), I (interval), lower intervals and upper

intervals, as well as BKB and BKA! (Give your answer in table)

REPORT SYSTEMATICS

BAB I PENDAHULUAN

1.1. Latar Belakang Praktikum

1.2. Tujuan Praktikum

1.3. Perumusan Masalah

1.4. Asumsi dan Batasan Masalah

1.5. Sistematika Laporan

BAB II LANDASAN TEORI

2.1. Data, Statistik dan Statistika

2.2. Peubah Acak

2.3. Fungsi Kepadatan Probabilitas

2.4. Jenis-jenis Distribusi Diskrit

2.4.1. Distribusi Seragam

2.4.2. Distribusi Binomial

2.4.3. Distribusi Hypergeometric

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2.4.4. Distribusi Binom Negative

2.4.5. Distribusi Geometric

2.4.6. Distribusi Poisson

2.4.7. Distribusi Bernoulli

2.5. Jenis-jenis Distribusi Kontinu

2.5.1. Distribusi Normal

2.5.2. Distribusi T

2.5.3. Distribusi F

2.5.4. Distribusi Chi-Square

2.5.5. Distribusi Weibull

2.5.6. Distribusi Lognormal

2.5.7. Distribusi Erlang

2.5.8. Distribusi Exponential

2.5.9. Distribusi Gamma

2.5.10. Distribusi Laplace

2.5.11. Distribusi Beta

2.5.12. Distribusi Triangular

2.5.13. Distribusi Cauchy

2.6. Distribusi Frekuensi

2.7. Pengujian Distribusi

2.8. P-Value

2.9. Jurnal Internet

BAB III METODOLOGI PENELITIAN

3.1. Lokasi dan Waktu Praktikum

3.2. Pengumpulan Data

3.3. Pengolahan Data

3.4. Analisis dan Evaluasi

3.5. Kesimpulan dan Saran

BAB IV PENGUMPULAN DATA

4.1. Pengumpulan Data Hasil Percobaan dan Flow Chart Distribusi Diskrit

4.2. Pengumpulan Data Badan Pusat Statistik (BPS) dan Flow Chart Distribusi

........Kontinu

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BAB V PENGOLAHAN DATA

5.1. Pengujian Distribusi terhadap Data dengan Menggunakan Uji Chi-Square

Tunggal

5.2. Pengujian Distribusi terhadap Data dengan Menggunakan Uji Chi-Square

Kelompok

BAB VI ANALISIS DAN EVALUASI

6.1. Analisis

6.2. Evaluasi

BAB VII KESIMPULAN DAN SARAN

7.1. Kesimpulan

7.2. Saran

DAFTAR PUSTAKA

LAMPIRAN

- Data Hasil Percobaan Diskrit

- Data Badan Pusat Statistik (BPS)

- Tabel Chi-Square

- Tabel P-Value

- Form Responsi Dosen

- Form Asistensi Laporan

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INTRODUCTION

The Analytic Hierarchy

Process (AHP) has been

developed by T. Saaty (1977,

1980, 1988, 1995) and is one of

the best known and most widely

used MCA approaches. It

allows users to assess the

relative weight of multiple

criteria or multiple options

against given criteria in an

intuitive manner. In case

quantitative ratings are not

available, policy makers or

assessors can still recognize

whether one criterion is more

important than another.

Therefore, pairwise comparisons

are appealing to users. Saaty

established a consistent way of

converting such pairwise

comparisons (X is more

important than Y) into a set of

numbers representing the

relative priority of each of the

criteria. The basic procedure to

carry out the AHP consists of the

following steps: 1) Decompose

the decision-making problem

into a hierarchy. 2) Make pair

wise comparisons and establish

priorities among the elements in

the hierarchy. 3) Synthesise

judgments (to obtain the set of

overall or weights weights for

achieving achieving your goal)

4) Evaluate and check the

consistency of judgements

Customer Satisfaction Research

is a research used to see how far

the level of customer

satisfaction of a product /

service by using assessment

instruments.. The Kano Model is

an insightful way of

understanding and

categorizing 5 types of

Customer Requirements (or

potential features) for new

products and services.

MULTI CRITERIA DECISION MAKING AND CUSTOMER

SATISFACTION RESEARCH

MODULE B

PRACTICUM

PROCEDURE

1. Determine the

products / services

that will be the object

of research

2. Decompose the

decision-making

problem into a

hierarchy

3. Determine the sample

4. Create an AHP

questionnaire and

kano questionnaire

5. Distributing

questionnaires

Tool : - Questionnaire

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CASE STUDY

Jasa Online Shop

TangibleAssurance EmpathyReliability Responsiveness

Bukalapak OLXLazada

Keamanan

Data Privasi

Transaksi

Jual-beli cepat

Barang Sesuai

Pesanan

Pilihan

Produk

Banyak

Jaminan

Produk Tepat

Waktu

Jaminan

Kondisi

Barang

Jaminan

Keamanan

Transaksi

Garansi

Website yang

Mudah

Ditelusuri

Tampilan

Website yang

Menarik

Gambar

Produk Jelas

Kategori

Produk

Kemudahan

Pemasaran

Produk

Kemudahan

Pencarian

Produk

Ketersediaan

Customer

Service

Keterlibatan

Karyawan

Informasi

Jelas

Kemampuan

Customer

Service

Baik

Respon

Kritik

Pengguna

Kesediaan

Promo-

promo

Survey conducted on 5 respondents and obtained data questionnaire level 2 as follows:

Data Responden Level 2

Responden 1

Element Reliability Assurance Tangible Empathy Responsiveness

Reliability 1 4 7 6 7

Assurance 1/4 1 7 5 1/4

Tangible 1/7 1/7 1 8 1/6

Empathy 1/6 1/5 1/8 1 1/4

Responsiveness 1/7 4 6 4 1

Responden 2

Elemen Reliability Assurance Tangible Empathy Responsiveness

Reliability 1 6 7 4 1/3

Assurance 1/6 1 1/5 4 5

Tangible 1/7 5 1 9 1/6

Empathy 1/4 1/4 1/9 1 1/4

Responsiveness 3 1/5 6 4 1

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Data Responden Level 2

Responden 3


Reliability 1 1/2 5 4 1/6

Assurance 2 1 5 4 1/5

Tangible 1/5 1/5 1 1/3 1/2

Empathy 1/4 1/4 3 1 4

Responsiveness 6 5 2 1/4 1

Responden 4


Reliability 1 5 6 1/4 8

Assurance 1/5 1 7 4 1/5

Tangible 1/6 1/7 1 9 7

Empathy 4 1/4 1/9 1 1/3

Responsiveness 1/8 5 1/7 3 1

Responden 5


Reliability 1 4 7 6 3

Assurance 1/4 1 4 2 1/2

Tangible 1/7 1/4 1 7 3

Empathy 1/6 1/2 1/7 1 1/3

Responsiveness 1/3 2 1/3 3 1

The geometric mean for the level 2 element between Reliability and Assurance can be calculated as

follows Responden 1 : 4

Responden 2 : 6

Responden 3 : 1/2

Responden 4 : 5

Responden 5 : 4

Then the geometric mean is:

=√4×6×1/2×5×45

=2,9926

The geometric mean of each element can be seen in the following table:

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Reliability 1,0000 2,9926 6,3458 2,7019 1,5632

Assurance 0,3342 1,0000 2,8738 3,6411 0,4782

Tangible 0,1576 0,3480 1,0000 4,3242 0,7816

Empathy 0,3701 0,2746 0,2313 1,0000 0,4884

Responsiveness 0,6397 2,0913 1,2794 2,0477 1,0000

Total 2,5016 6,7065 11,7302 13,7150 4,3113

Berikutnya, dibagi masing-masing angka di setiap sel dengan jumlah kolom masing-masing dan

menghasilkan matriks normalisasi dimana angka di setiap kolom berjumlah 1. Sementara bobot

diperoleh dari rata-rata setiap baris yang didapat dengan cara menjumlahkan dan membaginya

dengan jumlah data.

Tabel Matriks Normalisasi dan Rata-rata Baris untuk Elemen Level 2

Elemen Reliability Assurance Tangible Empathy Responsiveness bobot

Reliability 0,3997 0,4462 0,5410 0,1970 0,3626 0,3893

Assurance 0,1336 0,1491 0,2450 0,2655 0,1109 0,1808

Tangible 0,0630 0,0519 0,0852 0,3153 0,1813 0,1393

Empathy 0,1479 0,0410 0,0197 0,0729 0,1133 0,0790

Responsiveness 0,2557 0,3118 0,1091 0,1493 0,2319 0,2116

total 1,0000 1,0000 1,0000 1,0000 1,0000 1,0000

From the table above, it can be concluded that the weight of Reliability criteria is the highest with the

weight of 0.3893. This means that in choosing online shop services, consumers are most concerned

with Reliability criteria.

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PRACTICUM GOALS

1. Be able to understand the concept of AHP

2. Be able to apply the AHP concept in every decision with many criteria.

3. Be able to process the data obtained from the questionnaire with some statistical

methods.

4. Be able to choose the best solution from several options and selection criteria to

increase productivity

5. Be able to use Super Decision software.

6. Be Able to determine the category of consumer desire in increasing consumer

satisfaction.

7. Be Able to analyze the level of customer satisfaction of a product or service.

PRESENTATION MINIMUM

STANDARD:

1. The basic principle of AHP

2. Sampling techniques

3. Calculation of Weight, Consistency ratio

(CR) and CRH

4. Analysis Sensitivity

5. Validity and Reliability

6. Dimensions of product quality

7. Dimension of service quality

8. Kano Model

9. Satisfaction, Switching index and

Market Damage Analysis

REFERENCES

1. Gaspersz, Vincent. 2005. Total Quality

Management. Jakarta : PT Gramedia

Pustaka Utama.

2. Kusumadewi, Sry. 2006. Fuzzy Multi-

Attribute Decision Making.

Yogyakarta : Graha ilmu.

3. Saaty, Thomas L. 1993. Pengambilan

Keputusan. Jakarta : PT Pustaka

Binaman Pressindo.

4. Saaty, Thomas L. 1994. Fundamentals

of Decision Making and Priority

Theory. USA : WS Publication.

5. Sinulingga, Sukaria. 2012. Metode

Penelitian. Medan : USUpress

6. Tjiptono, Fandy. 2005. Prinsip-Prinsip

Total Quality Service. Yogyakarta :

And

Page | 20


1. Make a hierarchy of product/service with 4 levels (at least 3 products/services are

compared). Then calculate the weight of each criteria at level 2 and determine the

most important criteria.

2. Specify the attribute category of the following questionnaire:

No Pernyataan

Fungsional

Skala Pernyataan

Disfungsional

Skala

1 2 3 4 5 1 2 3 4 5

1 Tampilan Website

Menarik X Tampilan Website Tidak

Menarik X

2 Barang Diantar Sesuai

Pesanan X Barang Diantar Tidak

Sesuai dengan Pesanan X

3 Pilihan Produk Banyak X Pilihan Produk

Terbatas X

4

Jaminan Produk Diantar

Tepat Waktu X Jaminan Produk

Diantar Tidak Tepat

Waktu

X

5 Ada Jaminan Kondisi

Barang X Tidak Ada Jaminan

Kondisi Barang X

REPORT SYSTEMATICS

BAB I PENDAHULUAN

1.1. Latar Belakang

1.2. Maksud dan Tujuan





2.1. Penelitian Survei

2.2. Teknik Sampling

2.3. Metode Penentuan Jumlah Sampel

2.4. Hierarki

2.5. Dimensi Kualitas Produk

2.6. Dimensi Kualitas Jasa

2.7. Dasar-dasar AHP

2.7.1. Decomposition

2.7.2. Comperative Judgment

2.7.3. Synthesis of Priotity

2.7.4. Logical Consistency

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2.8. Konsistensi Hierarki

2.9. Analisis Sensitivitas

2.10. Uji Validitas

2.11. Uji Reliabilitas

2.12. Metode Kano

2.13. Method of Successive Interval

2.14. Market Damage Analysis (MDA)

2.15. Switching Index

2.16. Satisfaction Index

2.17. Software Super Decision


2.18.1. Jurnal Internet AHP

2.18.2. Jurnal Internet Kano


3.1. Lokasi dan Waktu Penelitian



3.4. Analisa dan Evaluasi



4.1. Hierarki Pemilihan Alternatif Jasa/Produk

4.2. Matriks Banding Berpasangan (Pairwise Comparison)

4.2.1. Level 2

4.2.2. Level 3

4.2.3. Level 4

4.3. Data Spesifikasi Responden AHP

4.4. Data Kuesioner Model Kano & RKP

4.5. Data Kuesioner Untuk Perhitungan MDA, Satisfaction Index, dan Switching

Index

4.6. Data Spesifikasi Responden Model Kano & RKP

Page | 22


5.1. Perhitungan Rata-Rata Pembobotan untuk Masing-masing Elemen dan

Unsur

5.2. Perhitungan Bobot Parsial dan Konsistensi Matriks

5.2.1. Level 2

5.2.2. Level 3

5.2.2.1. Unsur-Unsur dari Elemen

5.2.3. Level 4

5.3. Penentuan Bobot Prioritas untuk Alternatif

5.3.1. Perhitungan Bobot Level 3

5.3.2. Perhitungan Bobot Level 2

5.3.3. Perhitungan Total Bobot Produk/Jasa

5.4. Perhitungan Konsistensi Hierarki

5.4.1. Perhitungan Konsistensi Hierarki Level 2 : Elemen

5.5. Perhitungan dengan Software Super Decision

5.6. Analisis Sensitivitas AHP

5.6. Uji Validitas Model Kano

5.7. Uji Reliabilitas Model Kano

5.8. Perhitungan Model Kano

5.9. Penentuan Grade Atribut

5.10. Menentukan Nilai Customer Satisfaction (CS) dan Customer

Dissatisfaction(CD)

5.11. Pemetaan Atribut Mutu Pelayanan ke Peta Performansi

5.12. Pengelompokan Responden Berdasarkan MDA

5.13. Switching Index

5.14. Satisfaction Index

BAB VI ANALISA DAN EVALUASI

6.1. Analisa

6.2. Evaluasi


7.1. Kesimpulan

7.2. Saran

Page | 23

DAFTAR PUSTAKA

LAMPIRAN

- Hierarki (Ukuran A3)

- Foto Responden AHP

- Foto Responden Kano & RKP

- Kuesioner AHP

- Kuesioner Kano & RKP

- Tabel RI (Random Index)

- Tabel Product Moment

- Tabel Spearman Brown


- Form Asistensi Case

- Form Asistensi

Page | 24

INTRODUCTION

To study the

phenomenom of scientific

research is required.

Implementation of this

research can be done

through surveys,

experiments, or observations

in accordance with the

discilines studied. For that

required the design is really

adequate so that the

conclusions obtained later

can represent the

population. Experimental

design is present as one of

the solution is collecting

information from a scientific

experiment. Experimental

design is a experimental

(with every step of action

completely undefined) so

that information relating to

or required for the issue

under investigation can be

collected. The design of an

experiment aims to obtain or

collect as much information

as necessary and useful in

investigating the issues to be

discussed.

MODULE C

PRACTICUM PROCEDURE

Implementation of the

practicum performed in

accordance with work

procedures based on the

instructions of the assistant.

Before conducting the

experiment, praktikan shows

a literature study of related

experiments.

Data collection in

accordance with the

experimental results for

each interaction between

factors. Furthermore, the

data obtained from the

experimental design steps

to get the conclusions

about the units assessed.

The conclusions of these

values can be obtained by

comparing the results of

data processing with the

value of certain tables and

with the help of the use of

software SPSS and Minitab.

Page | 25

PRACTICUM GOALS

1. Be able to understand the notion of treatment, experimental error, and experimental

unit.

2. Be able to understand the basic principles in the design of the experiment.

3. Can do experimental design based on the steps that have been determined.

4. Can obtain or collect data necessary and useful in conducting research issues to be

discussed.

5. Can know the factors that affect the characteristics that have significant effects of an

experiment.

6. Knowing and understanding the concepts of regression and correlation.

7. Be able to formulate or create a regression formulation of a particular data.

8. Be able to calculate the level of relationship between data variables with correlation

analysis.

PRESENTATIO MINIMUM STANDARD:

1. Describe the defenitions experimental design and general experimental design

objectives are.

2. Describe about Treatment, Experimental Unit, Errors, and basic principles of experimental

design.

3. Describe about normality test and homogeneity test.

4. Describe the differences of the Completely Randomized Design Model and the

randomized Block Design.

5. Describe about Experimental Design Models.

6. Describe the theory of Regression and Correlation.

REFERENCES

1. Prof. Dr. Sudjana, MA, M.Sc , “Desain dan Analisis Eksperimen”

2. Dr.Ir. Kemas Ali Hanafiah, M. S. “Rancangan Percobaan Aplikatif”

3. Y, Suntoyo. “Percobaan Perancangan, Analisis dan Interpretasinya”

4. R. Walpole.”Pengantar Statistik”.

5. E. Sugandi, “Rancangan Percobaan”

Page | 26


1. Give and describe the example of the basic experimental principles of your group

case!

2. There are 4 times (morning, noon, afternoon and night) to teaching the statistical

subject in engineering students of industrial batch 2016. We want to investigate the

difference result of teaching method in the 4 times. Suppose there are 20 students on

the same basis that are made into experiments conducted with teaching methods

and the same material, held the exam. The results can be seen below.

Time

Morning Noon Afternoon Night

Sc

ore

70 68 52 46

68 60 49 41

78 64 48 44

75 58 58 43

72 62 60 42

From the data make the testing step hypothesis with 5% accuracy assumption and

Use the ANAVA table in the experiment above to determine whether or not the effect

of time effects on the results of teaching! Test BNJ on the above experimental results!

REPORT SYSTEMATICS

BAB I. PENDAHULUAN

1.1. Latar Belakang Masalah




1.5. Sistematika Penulisan Laporan

BAB II. LANDASAN TEORI

2.1. Desain Eksperimen

2.1.1. Tujuan Desain Eksperimen

2.1.2. Prinsip Dasar Eksperimen

2.2. Model Perancangan Eksperimen

2.2.1. Rancangan Acak Lengkap

2.2.2. Rancangan Acak Kelompok

2.2.3. Rancangan Bujur Sangkar Latin

Page | 27

2.3. Eksperimen Faktorial

2.4. Model Eksperimen

2.4.1. Model Acak

2.4.2. Model Tetap

2.4.3. Model Campuran

2.4.3.1. Model campuran a tetap, b dan c acak

2.4.3.2. Model campuran a dan b tetap, c acak

2.5. Metode Yates

2.6. Uji Rata-rata sesudah ANAVA

2.7. Uji Kenormalan Data

2.8. Uji Homogenitas Varian

2.8.1. Uji Fisher

2.8.2. Uji Bartlett

2.8.3. Koefisien Homogenitas

2.9. Teori Regresi

2.9.1. Defenisi Regresi

2.9.2. Jenis-jenis Regresi

2.9.3. Pengujian Regresi

2.9.4. Kelinieran Regresi

2.10. Teori Korelasi

2.10.1. Defenisi Korelasi

2.10.2. Jenis-Jenis Korelasi

2.10.3. Pengujian Hipotesis Korelasi

2.10.4. Koefisien Korelasi


BAB III. METODOLOGI PENELITIAN

3.1. Objek Penelitian

3.2. Lokasi dan Waktu Praktikum

3.3. Sumber Data



3.6. Analisa dan Evaluasi


Page | 28

BAB IV. PENGUMPULAN DATA

4.1. Prosedur Kerja

4.2. Alat dan Bahan

4.3. Penentuan Faktor-Faktor yang Digunakan

4.4. Hasil Pengukuran

BAB V. PENGOLAHAN DATA

5.1. Uji Kenormalan Data

5.2.1. Uji Kenormalan secara Manual

5.2.2. Uji Kenormalan dengan Minitab

5.2. Uji Homogenitas Varians

5.2.1. Uji Homogenitas Varians Untuk Setiap Faktor

5.2.2. Uji Homogenitas Varians Untuk Interaksi Faktor

5.3. Perhitungan ANAVA

5.3.1. Perhitungan Secara Manual

5.3.2 Perhitungan Koefisien Homogenitas

5.3.3. Perhitungan dengan Metode Yates (23)

5.3.4. Perhitungan Dengan Software SPSS

5.4. Uji Rata-rata sesudah ANAVA

5.5. Perhitungan Persamaan Regresi

5.5.1. Perhitungnan Secara Manual

5.5.2. Perhitungan dengan Software

5.6. Pengujian Kelinearan Regresi

5.6.1. Perhitungan Secara Manual

5.6.2. Perhitungan dengan Software SPSS

5.7. Perhitungan Fungsi Koefisien Korelasi

5.7.1. Perhitungan Koefisien Korelasi secara Manual

5.7.2. Perhitungan Koefisien Korelasi dengan Software SPSS

5.7.3. Perhitungan Koefisien Determinasi

5.8. Pengujian Hipotesis Korelasi

BAB VI. ANALISA DAN EVALUASI

6.1. Analisis

6.2. Evaluasi

Page | 29

BAB VII. KESIMPULAN DAN SARAN

7.1. Kesimpulan

7.2. Saran

DAFTAR PUSTAKA

LAMPIRAN

- Tabel Anderson Darling

- Tabel Fisher

- Tabel Barltlet

- Studi Literatur

- Form Asistensi

- Form Dosen

Page | 30

INTRODUCTION

The first queueing theory

problem was considered by

Erlang in 1908 who looked at

how large a telephone

exchange needed to be in

order to keep to a

reasonable value the

number of telephone calls

not connected because the

exchange was busy (lost

calls).

Queueing theory is the

mathematical study of

waiting lines, or queues. A

queueing model is

constructed so that queue

lengths and waiting time

can be predicted.

Queueing theory is generally

considered a branch of

operations research

because the results are often

used when making business

decisions about the

resources needed to

provide a service.

As we know queues are a

common every-day

experience. Queues form

because resources are

limited. In fact it makes

economic sense to have

queues.

There are many

performance parameters in

model such as the number

of people in system, the

average arrival rate, the

average service rate, which

are interrelated and affect

the optimal number of

servers.

QUEUEING THEORY

MODULE D

PRACTICUM PROCEDURE

You will observe the

queuing system

directly for eight

hours. Required

data are general

description , layout

of system, picture

of system, limitation

of system, arrival

time, service time,

finish time of

service, and

customer aspiration

time. The

instruments used

are checksheets,

stationery, digital

clocks, and

cameras.

Page | 31

PRACTICUM GOALS

1. Be able to understand the queuing system wich is observed.

2. Be able to determine the type of distribution of collected data .

3. Be able to determine the queuing model of the system wich is observed.

4. Be able to analyze the queuing system wich is observed manually and computerized.

5. Be able to determine the optimum number of servers from the queuing system wich is

observed.

6. Be able to perform the productivity engineering from queuing system analysis.

7. Be able to simulate the queue with the optimum number of servers of the system wich is

observed.

PRESENTATION MINIMUM

STANDARD:

1. General description of queuing theory.

2. The basic components of the queue.

3. The types of service disciplines in

queuing theory.

4. The basic process of queuing.

5. Definition of steady state system.

6. Application of Poisson and Exponential

distribution in queuing theory.

7. Kendall Notation.

8. Queuing analysis to determine the

optimal number of server.

9. The function of WinQSB software in

queuing theory.

10. The function of server optimal number

queuing system simulation.

REFERENCES

1. Queueing Theory

2. Queueing Theory: A Linear Algebraic

Approach

3. Fundamental of Queueing Theory

4. An Introduction to Queueing Theory:

Modelling and Analysis in Applications

5. Elements of Queueing Theory: Palm

Martingale Calculus and Stochastic

Recurrences

6. Operation Research

7. Fundamental Operation Research

8. Stochastic Processes

9. Teori Antrian Markovian: Pendekatan

Praktis

10. Dasar Teori Antrian untuk Kehidupan

Nyata

11. Statistik Industri dan Probabilitas

Page | 32


1. Determine the correct kendall notation in accordance with the following description:

- The arrival frequency distribution is Poisson

- Service Level distribution is Exponential

- The number of servers is 2

- The first served person is the first who enter the queuing system

- The maximum customer is 6 people

- Unlimited Population amount

2. Two cashiers in a supermarket have shown that the arrival distribution of custumer follows

poisson distribution with arrival rate is 30 customers per hour. The distribution of service

time follows exponential distribution with average service time is 3 minutes per customer.

- Make a kendall notation that matches the situation.

- Calculate the queuing analysis:

a. Utilization (ρ)

b. Probability that there are no customers in the system (Po)

c. Average number of customer in waiting line of service (Lq)

d. Average number of customer in the system (Ls)

e. Average time a customer spends in waiting line waiting for service (Wq)

f. Average time a customer spends in the system (Ws)

3. What is the cause of the queue (include with reference) and what is the right solution for

the case on problem 2.

REPORT SYSTEMATICS

BAB I PENDAHULUAN

1.1. Latar Belakang Praktikum






2.1. Distribusi Poisson

2.1.1. Definisi Distribusi Poisson

2.1.2. Gambaran Umum Distribusi Poisson

2.1.3. Aplikasi Distribusi Poisson dalam Teori Antrian

Page | 33

2.2. Distribusi Eksponensial

2.2.1. Definisi Distribusi Eksponensial

2.2.2. Gambaran Umum Distribusi Eksponensial

2.2.3. Aplikasi Distribusi Eksponensial dalam Teori Antrian

2.3. Teori Antrian

2.3.1. Proses Dasar Antrian

2.3.2. Steady State System

2.3.3. Model-model Sistem Antrian

2.3.3.1.Model M/M/s

2.3.3.2.Model M/G/s

2.3.3.3. Model G/G/s

2.3.4. Notasi dan Terminologi Antrian

2.3.5. Notasi Kendall

2.3.6. Analisis Sitem Antrian

2.3.6.1. Rata-Rata Tingkat Kedatangan Pelanggan

2.3.6.2. Rata-Rata Tingkat Pelayanan

2.3.6.3. Tingkat Utilisasi Sistem

2.3.6.4. Probabilitas Tidak Adanya Pelanggan didalam Sistem

(Po)

2.3.6.5. Rata-Rata Jumlah Pengunjung dalam Antrian (Lq)

2.3.6.6. Rata-Rata Jumlah Pengunjung dalam Sistem (Ls)

2.3.6.7. Rata-Rata Waktu Pengunjung dalam Antrian (Wq)

2.3.6.8. Rata-Rata Waktu Pengunjung dalam Sistem (Ws)

2.4. Software EasyFit

2.4.1. Fungsi dan Sejarah EasyFit

2.4.2. Simbol-simbol yang Digunakan dalam Pengolahan EasyFit

2.4.3. Langkah-langkah Pengujian Distribusi dengan Software EasyFit

2.5. Software WinQSB

2.5.1. Fungsi dan Sejarah WinQSB

2.5.2. Simbol-simbol yang Digunakan dalam Pengolahan WinQSB

2.5.3. Langkah-langkah Pengolahan

2.6. Simulasi Antrian

2.7.1. Tujuan Simulasi

2.7.2. Langkah-langkah Simulasi Sistem Antrian

Page | 34



3.2. Data yang Digunakan






4.1. Gambaran Umum Sistem Antrian

4.2. Layout, Foto Sistem, dan Foto Praktikan didalam Sistem Antrian

4.3. Batasan Sistem Antrian

4.4. Data Pengamatan

4.5. Data Frekuensi Kedatangan

4.6. Data Waktu antar Kedatangan

4.7. Data Waktu Tingkat Pelayanan

4.8. Waktu Tunggu Maksimum Pelanggan

4.8.1. Data Waktu Aspirasi Pelanggan

4.8.2. Penentuan Waktu Tunggu Maksimum Pelanggan


5.1. Pengujian Distribusi

5.1.1. Pengujian Distribusi terhadap Data Frekuensi Kedatangan

5.1.2. Pengujian Distribusi Terhadap Data Waktu Antar Kedatangan

5.1.3. Pengujian Distribusi Terhadap Data Waktu Tingkat Pelayanan

5.2. Menentukan Model Antrian dengan Notasi Kendall

5.3. Analisis Sistem Antrian

5.3.1. Menghitung Rata-rata Tingkat Kedatangan Pelanggan

5.3.2. Menghitung Rata-rata Tingkat Pelayanan

5.3.3. Menghitung Tingkat Utilisasi Sistem

5.3.4. Menghitung Probabilitas Tidak Adanya Pelanggan didalam

.Sistem

5.3.5. Menghitung Rata-rata Jumlah Pengunjung dalam Antrian

5.3.6. Menghitung Rata-rata Jumlah Pengunjung dalam Sistem

5.3.7. Menghitung Rata-rata Waktu Pengunjung dalam Antrian

5.3.8. Menghitung Rata-rata Waktu Pengunjung dalam Sistem

Page | 35

5.4. Analisis Sistem Antrian dengan Software WinQSB

5.5. Perhitungan Jumlah Server Optimum

5.6. Simulasi Sistem Antrian dengan Server Optimum

5.6.1. Perhitungan Peluang Kumulatif Data Frekuensi Kedatangan

5.6.2. Pembangkitan Bilangan Random

5.6.3. Penyusunan Bilangan Random dalam Simulasi

5.6.4. Penentuan Batasan Peluang Distribusi

5.6.5. Penyusunan Nilai Batasan Peluang Distribusi dalam Simulasi

5.6.6. Pembangkitan Bilangan Random Terhadap Data Waktu

Antar Kedatangan

5.6.7. Pembangkitan Bilangan Random Terhadap Data Waktu Tingkat

Pelayanan

5.6.8. Hasil Simulasi Sistem Antrian

5.6.9. Checksheet Hasil Simulasi Sistem Antrian

5.6.10. Analisis dengan Hasil Simulasi Sistem Antrian


6.1. Analisis

6.1.1. Analisis Data Frekuensi Kedatangan

6.1.2. Analisis Data Waktu antar Kedatangan

6.1.3. Analisis Data Waktu Tingkat Pelayanan

6.1.4. Analisis Model Antrian dengan Notasi Kendall

6.1.5. Analisis Sistem Antrian

6.1.6. Analisis Sistem Antrian Terkomputerisasi

6.1.7. Analisis Perhitungan Jumlah Server Optimum

6.1.8. Analisis Simulasi Sistem Antrian dengan Server Optimum

6.2. Evaluasi

6.2.1. Evaluasi Data Frekuensi Kedatangan

6.2.2. Evaluasi Data Waktu Antar Kedatangan

6.2.3. Evaluasi Data Waktu Tingkat Pelayanan

6.2.4. Evaluasi Model Antrian dengan Notasi Kendall

6.2.5. Evaluasi Sistem Antrian

6.2.6. Evaluasi Sistem Antrian Terkomputerisasi

6.2.7. Evaluasi Perhitungan Jumlah Server Optimum

6.2.8. Evaluasi Simulasi Sistem Antrian dengan Server Optimum

Page | 36


7.1. Kesimpulan

7.2. Saran

DAFTAR PUSTAKA

LAMPIRAN

- Data Tinjauan Pendahuluan

- Data Pengamatan Antrian

- Data Waktu Aspirasi

- CD Pengamatan

- Form Asistensi Case

- Form Asistensi Laporan


Page | 37

INTRODUCTION

In industrial world, there are

many systems that are so

complex, such as production

systems and inventory

systems. Until now if the

company wants to change

one of its systems to help or

make improvements, it will

take a very long time and

considerable cost, plus the

success of the

implementation of the system

in accordance or not with the

desired expectations. For that

we need to create a module

that studies the complex

system in order to make the

model. This activity is called

system modeling. This activity

is called system modeling.

Modeling the system can

help us to learn and

understand the complex

system in a fairly short time The

advantage gained from

modeling this system is that

we need a fairly short time

and low cost. Then we can

minimize the risk of failure of

the new system

implementation.

Model is defined as a

representation or abstraction

of an actual object or

situation. The model shows

both direct and indirect

relationships as well as

reciprocal links in cause and

effect terms. Since a model is

an abstraction of reality, it is

less complex than reality itself.

Thus, the model is a

simplification of a complex

reality. The system is defined

as the set or combination of

parts that make up a complex

unity.

MODELLING SYSTEM

MODULE E

PRACTICUM PROCEDURE

Softwares that we

used are Eviews 7

and Vensim.

Vensim is one of

the software for

system dynamics

model simulation,

which can be used

to simplify the

simulation of system

dynamics model.

Other software

designed to

simulate the

dynamics system

model include:

Promodel,

Dynamo, Powersim,

Stella, I-think.

Page | 38

PRACTICUM GOALS

1. Be able to observe and understand the real system that becomes the object of

observation.

2. Be able to determine the entities, activities, and attributes of the real system.

3. Be able to make the concept of relationship between each entity in the real system is

described in the causal loop diagram.

4. Be able to model the real system and make computer simulation model.

5. Be able to provide suggestion system in the form of improvements to the observed

system.

PRESENTATION MINIMUM STANDARD:

7. Describe the definitions, features, and types of systems.

8. Describe the definition, and the types of variables.

9. Describe the principles of system modeling.

10. Explain the definition of verification and validation and methods.

11. Explain the definition, purpose and benefits of simulation

12. Explain about the test of data stationarity.

13. Presenting an example of a model based on points above.

REFERENCES

1. Pemodelan Sistem (Togar M. Simatupang)

2. Modeling of Dynamic Systems (Lennart Ljung, Torkel Glad)

3. System Modelling and Simulation (V.P. Singh)

4. Pemodelan Sistem (Humala L. Napitupulu)

Page 39


1. Make a dynamic system, describe it in the form of a causal diagram and describe it in

detail in the chosen system:

a. Variables of the system

b. Principle of Modeling System that used to create the model.

c. Modelling Principle.

N/B: Adjust the explanation and components answer in the presentation slides.

2. Explain your opinion about the relationship / linkage of these following terms!

a. Verification and validation

b. Causal loop and correlation testing

c. Causal loop and logical conclusion

d. Stasionarity testing and simulation results

N/B: Both answers of questions above are not allowed to be as same as other

participants.

REPORT SYSTEMATICS

BAB I PENDAHULUAN

1.1. Latar Belakang

1.2. Tujuan Penelitian





2.1. Teori Mengenai Model

2.2. Teori Mengenai Sistem

2.3. Teori Mengenai Pemodelan Sistem

2.4. Teori Mengenai Prinsip-Prinsip Pemodelan Sistem

2.5. Teori Mengenai Tujuan dari Pemodelan Sistem

2.6. Teori Mengenai Uji Stasioner Data

2.7. Toeri Mengenai Eviews 7

2.8. Teori Mengenai Causal dan Causal Loop

2.9. Teori Mengenai Vensim

2.10. Teori Mengenai Logical Conclusion

2.11. Teori Mengenai System Dynamics

2.12. Teori Mengenai Variabel

Page 40

2.13. Teori Mengenai Besaran dan Satuan

2.14. Teori Mengenai Uji Verifikasi dan Validasi Model

2.15. Teori Mengenai Penentuan Jumlah Replikasi

2.16. Teori Mengenai Uji Korelasi

2.17. Teori Mengenai Sistem Persediaan






3.5. Analisa Dan Evaluasi

3.6. Kesimpulan Dan Saran


4.1. Sistem

4.1.1. Gambaran Umum Sistem

4.1.2. Block Diagram Sistem

4.2. Data Hasil Pengamatan terhadap Sistem

4.2.1. Data Periodik

4.2.2. Data Konstan


5.1. Causal Loop

5.1.1. Komponen Causal

5.1.2. Variabel Causal

5.1.3. Pengujian Stasioneritas Data

5.1.4. Atribut Sistem

5.1.5. Logical Conclusion

5.1.6. Causal Loop Awal

5.1.7...Pengujian Korelasi untuk Tiap-Tiap Elemen yang

terdapat Pada Causal Loop

5.1.7.1. Pengujian Secara Manual

5.1.7.2. Pengujian dengan Menggunakan Software

5.18. Causal Loop Hasil Pengujian Korelasi

Page 41

5.2. Main Model

5.2.1. Komponen Auxilary

5.2.2. Komponen Constant

5.3. Kuantifikasi Data

5.3.1. Konsep Formulasi Manual

5.3.2. Equation Window Software Vensim

5.3.3. Main Model yang telah Dikuantifikasi

5.4. Presentasi Awal

5.5. Penentuan Jumlah Replikasi

5.6. Verifikasi Model

5.7. Validasi Model

5.7.1. Validasi Model Perhitungan Manual

5.7.2. Validasi Model Perhitungan Softwarei

5.8. Prediksi Keadaaan Sistem


6.1. Analisis

6.1.1. Analisis Causal Loop

6.1.2. Analisa Main Model

6.1.3. Analisa Presentasi

6.1.4. Analisis Verifikasi Model

6.1.5. Analisis Validasi Model

6.1.6. Analisis Keadaan Sistem Usulan

6.2. Evaluasi


7.1. Kesimpulan

7.2. Saran

DAFTAR PUSTAKA

LAMPIRAN

- Causal loop yang telah mendapat ACC

- Data Pengamatan Langsung Terhadap Sistem

- Pengujian Distribusi untuk Penentuan Atribut Sistem

- Data Analisis Presentasi

- Tabel Distribusi t-sudent

- Tabel Augmented Dicky Fuller


- Form Asistensi

Page 42

INTRODUCTION

Around 1980 and early

1990, Motorola was one of the

United States and European

companies that competed

with Japanese companies. The

Motorola leader realizes that

their product quality is low and

does not have a quality

program. Finally decided to

pursue quality seriously. But in

1987, there was a new

approach that came from

Motorola's communications

section called Six Sigma.

Dua The two main things

involved in the Six Sigma

concept at Motorola are a

consistent way to go out and

compare the performance of

customer needs (Sigma

Measurement) and perfect

quality targets (Sigma Goals).

There are many meanings of Six

Sigma. Six Sigma is defined as a

sophisticated technological

tool used by stylists in improving

or developing processes or

products. Six Sigma is

interpreted because the key to

Six Sigma improvements uses

statistical methods, though not

as a whole talking about

statistics.

QUALITY CONTROL WITH SIX SIGMA APPROACH

MODULE F

PRACTICAL

PROCEDURES

1. Factory determination.

2. Determine the type of

disability.

3. Conduct interviews on

the factory.

4. Determining how to

sample.

5. Conduct sample

measurements.

Page 43

PRACTICUM GOALS

1. Understand the application of sampling techniques and sample determination

2. Understand the quality control system with Six Sigma method to improve productivity.

3. Analyze the quality of a manufactured product on the market.

PRESENTATION MINIMUM STANDARD

1. Sampling techniques and techniques

of determining the number of samples

in quality control

2. Concept of quality control with six

sigma approach.

3. Attribute and variable defect type.

4. Types and causes of variations.

5. The Six Sigma cycle, ie:

a. The method used in Define

b. The method used in Measure

(1) Types of control charts

c. The method used in Analyze

d. The method used in Improve

e. The method used in Control

REFERENCES

1. Statistik Six Sigma dengan Minitab,

Panduan Cerdas Inisiatif Kualitas.

Penulis : C. Tri Hendradi.

2. Quality Control. Fifth Edition.

Penulis : Dale H. dan Besterfield.

3. Applied Statistics and Probability for

Engineers. Third Edition.

4. Penulis : Douglas C. Montgomery.’

The Six Sigma Handbook.

Penulis : Thomas Pyzdek

5. The Six Sigma Way: Bagaimana GE,

Motorolla, dan Perusahaan Terkenal

Lainnya Mengasah Kinerja Mereka.

Penulis : Peter S. Pande, Robert P.

Newman, Roland R. Cavanagh.

6. The Six Sigma Handbook.







Penulis : C. Tri Hendradi.

8. Quality Control. Fifth Edition.

Penulis : Dale H. dan Besterfield.

9. Applied Statistics and Probability for

Engineers. Third Edition.

Penulis : Douglas C. Montgomery.

10. The Six Sigma Handbook.







Page | 44


A company that produces spikes wants to improve the quality of its products by using

Six Sigma method. The data provided by the company is Nonconformities data as in

the following Tebel. Make a map of control and analysis!

Sub Group Number of Inspection Frequency Number of

Nonconformities Part Description

1 20 II 3 7,12

2 15 - 0 -

3 20 I 1 1

4 15 III 4 5,9,12

5 20 I 1 6

6 15 - 0 -

7 20 - 0 -

8 15 - 0 -

9 20 - 0 -

10 15 III 4 4,5,9

11 20 II 2 5,9

12 15 - 0 -

13 20 - 0 -

14 15 I 1 6

15 20 - 0 -

16 15 II 1 7

17 20 II 2 9,14

18 15 I 1 4

19 20 - 0 -

20 15 I 1 10

REPORT SYSTEMATICS

BAB I PENDAHULUAN

1.1. Latar Belakang

1.2. Maksud dan Tujuan


1.4. Asumsi-asumsi yang Digunakan

1.5. Batasan Masalah



2.1. Penentuan Jumlah Sampel dan Teknik Sampling

2.1.1. Penentuan Jumlah Sampel

2.1.2. Teknik Sampling

Page | 45

2.1.2.1. Probability Sampling

2.1.2.2. Nonprobability Sampling

2.2. Kualitas dan Pengendalian Kualitas

2.3. Variasi

2.3.1. Jenis Variasi

2.3.2. Penyebab Variasi

2.3.2.1. Chance Cause

2.3.2.2. Assignable Cause

2.4. Six Sigma

2.4.1. Define

2.4.2. Measure

2.4.2.1. Nilai Six Sigma

2.4.2.2. Defect Per Million Opportunity

2.4.2.3. Peta Kontrol Atribut

2.4.2.3.1. Peta p

2.4.2.3.2. Peta np

2.4.2.3.3. Peta c

2.4.2.3.4. Peta u

2.4.2.4. Peta Kontrol Variabel

2.4.2.2.1. Peta X dan R

2.4.2.4.2. Peta X dan S

2.4.2.4.3. Peta I-MR

2.4.2.4.4. Peta Moving Average

2.4.2.4.4.1. Peta Moving Average Data

Individual

2.4.2.4.4.2. Peta Moving Average Data

Subgroup

2.4.2.4.5. Peta T2

2.4.2.4.6. Gage Run Chart

2.4.2.4.7. Gage Study Crossed

2.4.2.4.7.1. Gage R&R Study (Crossed)-

ANOVA

2.4.2.4.7.2. Gage R&R Study (Crossed)-

Xbar and R

2.4.3. Analyze

2.4.3.1. Cause Effect Diagram

Page | 46

2.4.3.2. Failure Mode Effect Analysis (FMEA)

2.4.3.3. Process Capability

2.4.3.4. Uji Rata-rata

2.4.4. Improve

2.4.5. Control

2.4.5.1. Standard Operational Procedure (SOP)

2.5. Seven Tools

2.6. New Seven Tools




3.2. Lokasi dan Tempat

3.3. Sumber Data






4.1. Gambaran Umum Perusahaan

4.1.1. Sejarah Singkat Perusahaan

4.1.2. Ruang Lingkup Usaha

4.1.3. Struktur Organisasi Perusahaan dan Sistem Pengupahan

4.2. Bahan yang Digunakan dalam Pembuatan Produk

4.2.1. Bahan Baku

4.2.2. Bahan Penolong

4.2.3. Bahan Tambahan

4.3. Mesin yang Digunakan dalam Pembuatan Produk

4.4. Uraian Proses Produksi, Flow Process Chart, Flow Diagram dan Layout.

4.4.1. Uraian Proses Produksi

4.4.2. Flow Procces Chart

4.4.3. Flow Diagram

4.4.4. Layout

4.5. Kapasitas Produksi dan Sistem Pengendalian Persediaan

4.6. Data Kecacatan Produksi dari Perusahaan

Page | 47

4.7. Pengumpulan Data Kecacatan Atribut dan Variabel

4.7.1. Pengumpulan Data Kecacatan Atribut

4.7.1.1. Prosedur Kerja Pengumpulan Data Atribut

4.7.1.2. Check Sheet Jumlah Produk Cacat

4.7.1.3. Stratifikasi Jumlah Kecacatan Produk

4.7.2. Pengumpulan Data Pengukuran Variabel

4.7.2.1.Prosedur Kerja dan Data Pengukuran Variabel dengan 3 Operator

dengan 2 Pengukuran pada 1 Dimensi per Subgrup

4.7.2.2.Prosedur Kerja dan Data Pengukuran Variabel dengan 20

Pengukuran pada 1 Part dan 1 Dimensi

4.7.2.3.Prosedur Kerja dan Data Pengukuran Variabel pada 2 Dimensi


5.1. Define

5.1.1. Stratifikasi Jumlah Kecacatan Produk

5.2. Measure

5.2.1. Control Chart Data Atribut

5.2.1.1. Peta np/p

5.2.1.2. Peta c/u

5.2.2. Analisis Pengukuran Data Atribut

5.2.2.1. Histogram Data Atribut

5.2.2.1.1. Histogram Jumlah Produk yang Cacat

5.2.2.1.2. Histogram Stratifikasi Kecacatan

5.2.2.2. Pareto Diagram Berdasarkan Pengukuran

5.2.2.3. Scatter Diagram dan Perhitungan Korelasi

5.2.2.3.1. Scatter Diagram dan Perhitungan Korelasi Jenis Cacat

dan Number of Nonconforming

5.2.3. Control Chart Data Variabel

5.2.3.1. Peta X dan R

5.2.3.2. Peta X dan S pada Diameter

5.2.3.3. Peta I-MR

5.2.3.4. Peta Moving Average

5.2.3.4.1. Peta Moving Average Data Individual

5.2.3.4.2. Peta Moving Average Data Subgrup

5.2.3.5. Peta T2

5.2.4. Analisis Sistem Pengukuran Data Variabel

Page | 48

5.2.4.1. Gage Run Chart

5.2.4.2. Gage Study Crossed

5.2.4.2.1. Gage R&R Study (Crossed)-ANOVA

5.2.4.2.2. Gage R&R Study (Crossed)-Xbar and R

5.2.5. Perhitungan Defects Per Opportunity

5.2.6. Penentuan Nilai Six Sigma

5.3. Analyze

5.3.1. Analyze Atribut

5.3.1.1. Identifikasi Masalah dengan Cause Effect Diagram

5.3.1.2. Failure Mode Effect Analysis (FMEA)

5.3.2. Analyze Variabel

5.3.2.1. Uji Kenormalan Data Variabel

5.3.2.2. Process Capability

5.3.2.3. Uji Rata-rata

5.4. Improve

5.4.1. Menetapkan Sasaran Improvement

5.4.2. Memberikan Alternatif untuk Perbaikan

5.5. Control

5.5.1. Standard Operational Procedure (SOP)


6.1. Analisis

6.1.1. Analisis Define

6.1.2. Analisis Measure

6.1.3. Analisis Analyze

6.1.4. Analisis Improve

6.1.5. Analisis Control

6.2. Evaluasi

6.2.1. Evaluasi Define

6.2.2. Evaluasi Measure

6.2.3. Evaluasi Analyze

6.2.4. Evaluasi Improve

6.2.5. Evaluasi Control

Page | 49


7.1. Kesimpulan

7.2. Saran

DAFTAR PUSTAKA

LAMPIRAN

- Work Sheet Pengumpulan Data

- Flow Process Chart (A1)

- Flow Diagram (A1)

- Layout (A1)

- Tabel DPMO

- Tabel ANSI

- Tabel Chi-Square

- Tabel z



LAMPIRAN



Contoh Sampul Depan Laporan

LAPORAN PRAKTIKUM

STATISTIKA INDUSTRI

Oleh

KELOMPOK .... /GELOMBANG...

1. . . . . . . . . . . . . . 04030 . . .

2. . . . . . . . . . . . . . 04030 . . .

3. . . . . . . . . . . . . . 04030 . . .

4. . . . . . . . . . . . . . 04030 . . .

5. . . . . . . . . . . . . . 04030 . . .

L A B O R A T O R I U M P E N G U K U R A N D A N S T A T I S T I K

D E P A R T E M E N T E K N I K I N D U S T R I

F A K U L T A S T E K N I K

UNIVERSITAS SUMATERA UTARA

M E D A N

2017



Contoh Lembar Pengesahan Laporan

LAPORAN PRAKTIKUM

STATISTIKA INDUSTRI

Oleh


1. . . . . . . . . . . . . . 04030 . . .

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5. . . . . . . . . . . . . . 04030 . . .

Diketahui

Kepala Laboratorium

Pengukuran & Statistik

Ir. Khawarita Siregar, MT





M E D A N

2017



Contoh Sampul Depan Modul

MODUL A

PENGUJIAN DISTRIBUSI

Oleh


1. . . . . . . . . . . . . . 04030 . . .

2. . . . . . . . . . . . . . 04030 . . .

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4. . . . . . . . . . . . . . 04030 . . .

5. . . . . . . . . . . . . . 04030 . . .





M E D A N

2017



Contoh Lembar Pengesahan

MODUL A

PENGUJIAN DISTRIBUSI

Oleh


1. . . . . . . . . . . . . . 04030 . . .

2. . . . . . . . . . . . . . 04030 . . .

3. . . . . . . . . . . . . . 04030 . . .

4. . . . . . . . . . . . . . 04030 . . .

5. . . . . . . . . . . . . . 04030 . . .

Disetujui

Asisten Laboratorium

Pengukuran & Statistik

.......................





M E D A N

2017

Documents

MEASUREMENT AND STATISTIC LABORATORY - industri…industri.usu.ac.id/images/Image/lab/MODUL-FULL.pdf · PRESENTATION MINIMUM STANDARD 1. Describes definitions & types of data 2. Describes