measurement and statistic laboratory · 2020. 8. 13. · his/her slide score. 2) late more than 10...
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MEASUREMENT AND STATISTIC LABORATORY
INDUSTRIAL ENGINEERING DEPARTMENT - USU
Page | 2
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
MEASUREMENT AND STATISTIC LABORATORY
INDUSTRIAL ENGINEERING DEPARTMENT - USU
Page | 3
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
MEASUREMENT AND STATISTIC LABORATORY
INDUSTRIAL ENGINEERING DEPARTMENT - USU
Page | 4
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).
3) Late more than 30 minutes, will be
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.
2) Late more than 10 minutes, will be
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.
MEASUREMENT AND STATISTIC LABORATORY
INDUSTRIAL ENGINEERING DEPARTMENT - USU
Page | 5
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
MEASUREMENT AND STATISTIC LABORATORY
INDUSTRIAL ENGINEERING DEPARTMENT - USU
Page | 6
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
MEASUREMENT AND STATISTIC LABORATORY
INDUSTRIAL ENGINEERING DEPARTMENT - USU
Page | 7
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]
Hp. [082232417971]
Hp. [081394648379]
MEASUREMENT AND STATISTIC LABORATORY
INDUSTRIAL ENGINEERING DEPARTMENT - USU
Page | 8
Assistant
Winston
Member Library M. Faisal Ardiansyah
Member Equipment Tommy Wijaya
Member Equipment
081322251735 [email protected]
085276697578 [email protected]
082370228209 [email protected]
Inggrid Marcelina MS Member Library
Feby Sana Sibarani
Member ISO Nurul Hidayati
Member Cleanliness
082274934442 [email protected]
081363332486 [email protected]
081361695512 [email protected]
MEASUREMENT AND STATISTIC LABORATORY
INDUSTRIAL ENGINEERING DEPARTMENT - USU
Page | 9
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
Page | 10
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
Page | 11
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.
Page | 12
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
Page | 14
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
Page | 15
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
Page | 16
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
Page | 17
Data Responden Level 2
Responden 3
Elemen Reliability Assurance Tangible Empathy Responsiveness
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
Elemen Reliability Assurance Tangible Empathy Responsiveness
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
Elemen Reliability Assurance Tangible Empathy Responsiveness
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:
Page | 18
Elemen Reliability Assurance Tangible Empathy Responsiveness
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.
Page | 19
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
PRELIMINARY ASSIGNMENT
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
1.3. Perumusan Masalah
1.4. Asumsi dan Batasan Masalah
1.5. Sistematika Laporan
BAB II LANDASAN TEORI
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
Page | 21
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. Jurnal Internet
2.18.1. Jurnal Internet AHP
2.18.2. Jurnal Internet Kano
BAB III METODOLOGI PENELITIAN
3.1. Lokasi dan Waktu Penelitian
3.2. Pengumpulan Data
3.3. Pengolahan Data
3.4. Analisa dan Evaluasi
3.5. Kesimpulan dan Saran
BAB IV PENGUMPULAN DATA
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
BAB V PENGOLAHAN DATA
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
BAB VII KESIMPULAN DAN SARAN
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 Responsi Dosen
- 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
PRELIMINARY ASSIGNMENT
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
1.2. Tujuan Praktikum
1.3. Perumusan Masalah
1.4. Asumsi dan Batasan 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
2.11. Jurnal Internet
BAB III. METODOLOGI PENELITIAN
3.1. Objek Penelitian
3.2. Lokasi dan Waktu Praktikum
3.3. Sumber Data
3.4. Pengumpulan Data
3.5. Pengolahan Data
3.6. Analisa dan Evaluasi
3.7. Kesimpulan dan Saran
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
PRELIMINARY ASSIGNMENT
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
1.2. Tujuan Praktikum
1.3. Perumusan Masalah
1.4. Asumsi dan Batasan Masalah
1.5. Sistematika Laporan
BAB II LANDASAN TEORI
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
BAB III METODOLOGI PENELITIAN
3.1. Lokasi dan Waktu Penelitian
3.2. Data yang Digunakan
3.3. Pengumpulan Data
3.4. Pengolahan Data
3.5. Analisis dan Evaluasi
3.6. Kesimpulan dan Saran
BAB IV PENGUMPULAN DATA
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
BAB V PENGOLAHAN DATA
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
BAB VI ANALISIS DAN EVALUASI
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
BAB VII KESIMPULAN DAN SARAN
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
- Form Responsi Dosen
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
PRELIMINARY ASSIGNMENT
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
1.3. Perumusan Masalah
1.4. Asumsi dan Batasan Masalah
1.5. Sistematika Laporan
BAB II LANDASAN TEORI
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
BAB III METODOLOGI PENELITIAN
3.1. Lokasi dan Waktu Penelitian
3.2. Objek Penelitian
3.3. Pengumpulan Data
3.4. Pengolahan Data
3.5. Analisa Dan Evaluasi
3.6. Kesimpulan Dan Saran
BAB IV PENGUMPULAN DATA
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
BAB V PENGOLAHAN DATA
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
BAB VI ANALISIS DAN EVALUASI
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
BAB VII KESIMPULAN DAN SARAN
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 Responsi Dosen
- 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 : Thomas Pyzdek
7. The Six Sigma Way: Bagaimana GE,
Motorolla, dan Perusahaan Terkenal
Lainnya Mengasah Kinerja Mereka.
Penulis : Peter S. Pande, Robert P.
Newman, Roland R. Cavanagh.
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.
Penulis : Thomas Pyzdek
11. The Six Sigma Way: Bagaimana GE,
Motorolla, dan Perusahaan Terkenal
Lainnya Mengasah Kinerja Mereka.
Penulis : Peter S. Pande, Robert P.
Newman, Roland R. Cavanagh.
Page | 44
PRELIMINARY ASSIGNMENT
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
1.3. Perumusan Masalah
1.4. Asumsi-asumsi yang Digunakan
1.5. Batasan Masalah
1.6. Sistematika Laporan
BAB II LANDASAN TEORI
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
2.7. Jurnal Internet
BAB III METODOLOGI PENELITIAN
3.1. Objek Penelitian
3.2. Lokasi dan Tempat
3.3. Sumber Data
3.4. Pengumpulan Data
3.5. Pengolahan Data
3.6. Analisis dan Evaluasi
3.7. Kesimpulan dan Saran
BAB IV PENGUMPULAN 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
BAB V PENGOLAHAN DATA
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)
BAB VI ANALISIS DAN EVALUASI
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
BAB VII KESIMPULAN DAN SARAN
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
MEASUREMENT AND STATISTIC LABORATORY
INDUSTRIAL ENGINEERING DEPARTMENT - USU
LAMPIRAN
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MEASUREMENT AND STATISTIC LABORATORY
INDUSTRIAL ENGINEERING DEPARTMENT - USU
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Diketahui
Kepala Laboratorium
Pengukuran & Statistik
Ir. Khawarita Siregar, MT
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
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MEASUREMENT AND STATISTIC LABORATORY
INDUSTRIAL ENGINEERING DEPARTMENT - USU
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Disetujui
Asisten Laboratorium
Pengukuran & Statistik
.......................
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
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F A K U L T A S T E K N I K
UNIVERSITAS SUMATERA UTARA
M E D A N
2017