Binus University Repository

Suharjito, Suharjito

English

Push Notification System to Mobile Game Player Using Distributed Event-Based System Approach

Girsang, Abba Suganda

? ??????
???????? ??? ?????
???????
???????????
???????????
?????????????????
???????????????????????????????
???? ???????????
???? ?????? ? ?????? ??????????? ?????? ??????? ??? ? ??????? ?????????
?????
? ?????? ?????? ??
??????? ?????????? ??????
? ???? ??? ??? ???
???????????
???????????
????? ??? ??????????????
???????????? ??????????
? ??? ?? ? ????? ?
 i 
KICSS 2016 
The Eleventh International Conference on Knowledge, Information and Creativity Support 
Systems aims to facilitate technology and knowledge exchange between International 
researchers/scholars in the field of knowledge science, information systems, and system science 
and creativity support systems intensively in November 2016.  The conference covers a broad 
range of research topics in the fields of knowledge engineering and science, information 
technology, creativity support systems and complex system modeling. 
The conference has so far been an important event and has attracted many scientists, engineers 
and researchers from academia, government laboratories, and industry internationally. 
Following the tradition of previous conferences on Knowledge, Information and Creativity 
Support Systems, KICSS 2016 will cover all aspects of knowledge management, knowledge 
engineering, intelligent information systems, and creativity in an information technology 
context, including computational creativity and its cognitive and collaborative aspects. Papers 
reporting original unpublished research results on theoretical foundations, IT implementations of 
decision support and expert systems, as well as case studies of successful applications of the 
above-mentioned ideas in various fields are equally solicited. 
The main objectives of KICSS are to: 
1.! Disseminate the state-of-the-art information and knowledge on scientific findings in the 
area of Knowledge, Information and Creativity Support Systems, 
2.! Initiate working network and offer appropriate platform for knowledge sharing and 
exchange among local and international academicians, researchers, engineers and 
industrial practitioners who are actively involved in the field of Knowledge, Information 
and Creativity Support Systems, 
More than 150 papers have been submitted and 57 papers have been accepted and will be 
presented in the two-day conference which gathers participants, keynote and plenary speakers 
from Indonesia, Japan, Malaysia, German, and Thailand. It is hoped that this conference will 
provide a golden opportunity for everyone to discuss and share ideas on issues pertaining to 
product designs and concurrent engineering for the benefit of the learning institutions, 
companies and global society at large. 
 ii 
FOREWORD BY THE RECTOR OF BINA NUSANTARA UNIVERSITY 
In the current global economy which embraces the Knowledge, Information and Creativity 
Support Systems with more contribution to the Knowledge, Information and Creativity Support 
Systems.  It is our belief that the Knowledge, Information and Creativity Support Systems can 
provide the right tools and methodology to the science and knowledge in enhancing the level of 
education in their field. The KICSS also provides the platform for experts in the Knowledge, 
Information and Creativity Support Systems to exchange and generate ideas in transformation 
towards the global education.   
I would like to congratulate the Binus Graduate Program, Binus University, Japan Advanced 
Institute of Science and Technology (JAIST) and Jalap Creativity Society (JCS) for their effort in 
organizing the KICSS 2016. To all speakers, presenters and participants, I thank you all for 
participating and welcome you to this conference. The success of this conference depends on 
your participation and contribution in promoting the Knowledge, Information and Creativity 
Support Systems philosophies and activities as well as fostering closer mutual ties between the 
academicians, industry and society at large.  
All the best to KICSS 2016. 
PROFESSOR DR. IR. HARJANTO PRABOWO, MM 
Rector, Bina Nusantara University 
 iii 
FOREWORD BY THE CHAIRMAN OF KICSS 2016 
I would like to extend my warmest welcome to all keynote and plenary speakers, paper 
presenters and participants of KICSS 2016.  It is a great pleasure to have you all here. 
The Eleventh International Conference on Knowledge, Information and Creativity Support 
Systems aims to facilitate technology and knowledge exchange between International 
researchers/scholars in the field of knowledge science, information systems, and system science 
and creativity support systems intensively in November 2016. The conference covers a broad 
range of research topics in the fields of knowledge engineering and science, information 
technology, creativity support systems and complex system modeling. 
I would like to thank all the KICSS 2016 main and working committees for their dedication and 
hard work in making this conference a successful and memorable event, particularly the Binus 
Graduate Program, Bina Nusantara University, Japan Advanced Institute of Science and 
Technology (JAIST) and Jalap Creativity Society (JCS). 
On behalf of the committee, I would like to thank the Research & Technology Transfer Office, 
Binus University for the continuous support to this conference event.  Last but not the least, I 
express my gratitude to the paper reviewers and contributors for your endless support. 
Thank you very much. 
DR. FORD LUMBAN GAOL 
Chairman of KICSS 2016 
 iv 
PROGRAM COMMITTEES 
Honorary Chairs 
Harjanto Prabowo (Bina Nusantara University, Indonesia) 
Hiroshi Motoda (Osaka University, Japan) 
Susumu Kunifuji (JAIST, Japan) 
General Chairs 
Ford Lumban Gaol (Bina Nusantara University, Indonesia) 
Thanaruk Theeramunkong (SIIT, Thailand) 
George Angelos Papadopo (University of Cyprus, Cyprus) 
International Advisory Committee 
Chang Duk Jung (Korea University, Korea) 
Ho Tu Bao (JAIST, Japan) 
Kok-Leong Ong (Deakin University, Australia) 
Pham Tho Hoan (Hanoi National U. of Education, Vietnam) 
Sanparith Marukatat (NECTEC, Thailand) 
Takeo Higuchi (IMS Institute, Japan) 
Thomas Koehler (Dresden University of Technology, Germany) 
Vincent C.S. Lee (Monash University, Australia) 
Xijin Tang (Chinese Academy of Sciences, China) 
Organizing Chair 
Benfano Soewito (Bina Nusantara University, Indonesia) 
Agung Trisetyarso (Bina Nusantara University, Indonesia) 
Spits Warnars Harco Leslie Hendric (Bina Nusantara University, Indonesia) 
Workshop Chair 
Fergyanto E. Gunawan (Bina Nusantara University, Indonesia) 
Publication Chairs 
I Gede Putra Kusuma Negara (Bina Nusantara University, Indonesia) 
Sani Muhammad Isa (Bina Nusantara University, Indonesia) 
Ikhwan Jambak (Bina Nusantara University) 
Sfenrianto (Bina Nusantara University) 
Financial Chair 
Diana (Bina Nusantara University, Indonesia) 
Publicity Chairs 
Tessai Hayama (Nagaoka U of Technology, Japan) 
Jarot S. Suroso (Bina Nusantara University) 
Abba Suganda Girsang (Bina Nusantara University) 
Manik Hapsara (Bina Nusantara University) 
Ahmad Nurul Fajar (Bina Nusantara University) 
 v 
Technical Program Chairs 
Kiyota Hashimoto (PSU, Thailand) 
Takaya Yuizono (Japan Advanced Institute of Science and Technology (JAIST), Japan) 
Suharjito (Bina Nusantara University, Indonesia) 
Taufik (Bina Nusantara University, Indonesia) 
Technical Program Committees: 
Antoni Ligeza (AGH University of Science and Technology, Poland) 
Ateet Bhalla (Independent Consultant, India) 
Atsuo Yoshitaka (Japan Advanced Institute of Science and Technology (JAIST), Japan) 
Aziz Nanthaamornphong (Prince of Songkla University, Phuket Campus, Thailand) 
Brijesh Verma (Central Queensland University, Australia) 
Choochart Haruechaiyasak (National Electronics and Computer Technology Center - 
NECTEC, Thailand) 
Christopher John Hinde (Loughborough University, UK) 
Chutima Beokhaimook (Rangsit University, Thailand) 
Eakasit Pacharawongsakda (Sirindhorn International Institute of Technology (SIIT), 
Thammasat University, Thailand) 
Hsu-Shih Shih (Tamkang University, Taiwan) 
Ithipan Methasate (National Electronics and Computer Technology Center - NECTEC, 
Thailand) 
Joao Climaco (University of Coimbra, Portugal) 
Josef Jablonsky (University of Economics Prague, Czech Republic) 
Katsuhide Fujita (Tokyo University of Agriculture and Technology, Thailand) 
Kazuhiko Tsuda (Tsukuba University, Japan) 
Kazushi Nishimoto (Japan Advanced Institute of Science and Technology (JAIST), Japan) 
Kiyota Hashimoto (Prince of Songkla University, Thailand) 
Kritsada Sriphaew (Rangsit University, Thailand) 
Makoto Okada (Osaka Prefecture University, Japan) 
Marut Buranarach (National Electronics and Computer Technology Center - NECTEC, 
Thailand) 
Masaki Samejima (Osaka University, Japan) 
Naoki Fukuta (Shizuoka University, Japan) 
Nattapong Tongtep (Prince of Songkla University, Thailand) 
Nguyen Duy Hung (Sirindhorn International Institute of Technology (SIIT), Thammasat 
University, Thailand) 
Nichnan Kittiphattanabawon (Walailak University, Thailand) 
Nongnuch Ketui (Rajamagala University of Technology Lanna (RMUTL), Nan, Thailand) 
Pakinee Aimmanee (Sirindhorn International Institute of Technology (SIIT), Thammasat 
University, Thailand) 
Pawel Rotter (AGH University of Science and Technology, Poland) 
Przemyslaw Pukocz (AGH University of Science and Technology, Poland) 
Ryoju Hamada (Sirindhorn International Institute of Technology (SIIT), Thammasat 
University, Thailand) 
Ryszard Tadeusiewicz (AGH University of Science and Technology, Poland) 
Sanparith Marukatat (National Electronics and Computer Technology Center - NECTEC, 
Thailand) 
Satoshi Takahashi (The University of Electro-Communications, Japan) 
 vi 
Susumu Kujifuji (Japan Advanced Institute of Science and Technology (JAIST), Japan) 
Takanobu Otsuka (Nagoya Inssitute of Technology, Japan) 
Takashi Yoshino (Wakayama University, Japan) 
Takeo Higuchi (IMS Institute, Japan) 
Teerayut Horanont (Sirindhorn International Institute of Technology (SIIT), Thammasat 
University, Thailand) 
Tessai Hayama (Kanazawa Institute of Technology, Japan) 
Thatsanee Charoenporn (Burapha University, Thailand) 
Thepchai Supnithi (National Electronics and Computer Technology Center - NECTEC, 
Thailand) 
Thomas Koehler (TU Dresden, Germany) 
Tokuro Matsuo (Advanced Institute of Industrial Technology, Japan) 
Tomoharu Nakashima (Osaka Prefecture University, Japan) 
Tomomichi Hayakawa (Nagoya Institute of Technology, Japan) 
Tran Dang Hung (Hanoi National University of Education, Vietnam) 
Vilas Wuwongse (Asian University, Thailand) 
Vincent CS Lee (Monash University, Australia) 
Virach Sornlertlamvanich (Sirindhorn International Institute of Technology (SIIT), 
Thammasat University, Thailand) 
Wen Zhang (Beijing Univerisity of Chemical Technology, China) 
Xijin Tang (CAS Academy of Mathematics and Systems Science) 
Yoshinori Miyazaki (Shizuoka University, Japan) 
 vii 
TABLE OF CONTENTS 
Foreword by the Rector of Bina Nusantara University 
Foreword by the Chairman of Kicss 2016 
Program Committees 
ii 
iii 
iv 
ID Title/Authors 
101 Barrier of HIS Usage Level in Hospital with DEMATEL as Analysis 
Methodology, Case Study from A Private Hospital in Indonesia 
Made Dhanar. I. Y, Muhammad Reza, Meyliana, Achmad Nizar Hidayanto, Henry 
A. E. Widjaja 
1 
102 Power Consumption for Security on Mobile Devices 
Benfano Soewito, Fergyanto E. Gunawan, Diana, Anna Antonyova 
6 
103 Effectiveness of Analogue Business Game for Learning Elementary Corporate 
Management: An Experimental Approach 
Tomomi Kaneko, Masahiro Hiji, Ryoju Hamada 
10 
104 Disruptive Innovations Dynamics 
Agung Trisetyarso, Fithra Faisal Hastiadi 
16 
105 A Case Study about the Availability of the KJ Method Application for the 
collaborative theme search based on Database of the Patent Information 
Minoru Koide 
21 
106 Business Intelligence Model Development to Maximize University Lecturer 
Performance Measurement 
Santy, Eka Miranda, Julisar 
26 
107 A collaborative active learning for enhancing creativity for multiple 
disciplinary problems 
Taizo Miyachi, Saiko Iga, Mitsuo Hirokawa, Takashi Furuhata 
32 
108 A Design of Distributed Brainstorming Support Tool with Gamification 
Elements 
Hiroaki Furukawa, Takaya Yuizono, Akira Sakai 
38 
109 University Evaluation by Open Data, a case study on Effect of International 
Exchange Agreement 
Hiroaki Furukawa, Takaya Yuizono, Akira Sakai 
44 
110 Proposed Data Audit System Enhances Use in Accordance with the Data 
Owner's Policy 
Toshihiro Uchibayashi, Masahiro Hiji, Takuo Suganuma, Yuichi Hashi, Seira 
Hidano 
48 
111 Comparative Analysis of Grounded Theory and Affinity Diagram 
Nay Zar Aung, Takaya Yuizono, Youji Kohda 
53 
 viii 
112 Information Extraction and Integration for Enriching Cultural Heritage 
Collections. 
Watchira Buranasing, Suronapee Phoomvuthisarn, Marut Buranarach 
58 
113  Investigation of a Team-based activity Support System used in a Project-
based Learning Course 
Tessai Hayama, Saki Hayashi, Yuichi Kondo 
64 
114 Difficulty of Words and Their Ambiguity Estimated from the Result of Word 
Sense Disambiguation 
Takahiko Suzuki, Koki Miyata, Sachio Hirokawa 
70 
115 Mindsets of Craftsman: From barrier to lever 
Deny Willy Junaidy, Yukari Nagai 
75 
117 Research methods’ education: empirical testing of a technique for assessing 
prior knowledge – experiment B. 
Thomas Köhler, Bahaaeldin Mohamed, Thomas Köhler, Thorsten Claus 
81 
118 Analysis Service Quality in E-Marketplace As the Third Party Transaction 
Beta Centauri, Sfenrianto 
86 
119 Addressing Software Product Line Supply Chain Government System 
Ahmad Nurul Fajar, Ditdit Nugeraha Utama 
92 
120 Robust Adaptive Genetic K-Means Algorithm Using Greedy Selection for 
Clustering 
Abba Suganda Girsang, Fidelson Tanzil, Yogi Udjaja 
96 
121 Improvement of E-learning Quality by Means of a Recommendation System 
Fergyanto E. Gunawan, Aji Maryanto, Yogi Udjaja, Sevenpri Candra, Benfano 
Soewito 
101 
122 Comparison of Waterfall and Rational Unified Process in Software 
Development Project Management 
Tan Ci Bui, Jarot S. Suroso, Diana 
105 
123 Rational Unified Process Overview and Comparison with Waterfall and 
Extreme Programming 
Noviantho, Jarot S. Suroso, Tuga Mauritsius 
110 
125 Simulation of Rice Stock using System Dynamics Modeling 
Togar Alam Napitupulu, Edi Abdurachman 
116 
126 Acceptance Evaluation of e-Learning by Using UTAUT Method at ICT 
Solutions Company 
Aigis Arira, Nilo Legowo 
122 
128 Replication System of Oracle Database Standard Editionby Utilizing 
Traditional Archived Log 
Aria Sukma, Suharjito 
129 
129 Analysis for Determinants of Successful Student's GPA Achievement 
Angellia Debora Suryawan, Eryco Putra 
133 
 ix 
130 The effectiveness and suitability of MOOCs hybrid learning: A case study of 
public schools in Thai rural area 
Titie Panyajamorn, Youji Kohda, Pornpimol Chongphaisal, Thepchai Supnithi 
139 
135 A Study and Model of Automatic Mapper For ICD-9 and ICD-10 For 
Healthcare Provider in Indonesia 
Yohan Muliono, Ida Sri Rejeki Siahaan, Suharjito 
145 
136 Privacy Modelling of Sensitive Data in Universal Healthcare Coverage in 
Indonesia 
Irvan Santoso, Ida Sri Rejeki Siahaan, Suharjito 
150 
137 Implementation Model of Smart Connected Motorbike Service: An 
Engineering Economic Analysis of Machine to Machine Application 
Eva Nurul Jamilah, Jarot S. Suroso 
156 
139 Passing the Initiative for Visualizing Data to Readers 
Kazuo Misue and Rei Watanabe 
163 
141 Influences of the Social System to TQM 
Gunawan Wang 
169 
144 Project Management: Model Research in Improving Success Rate of a Digital 
Start-Up Project 
Herry Karmito, Bachtiar H. Simamora, Rudi, Nilo Legowo 
175 
145 Artificial Bee Colony – Based for Dietary Recommendation in Daily Nutrition 
Requirements 
Fidelson Tanzil, Lili A. Wulandhari, Sani M. Isa 
181 
147 Effect of the Number of Parents on the Performance of Multi-Parent Genetic 
Algorithm 
Seng Pan That Pann Phyu, Gun Srijuntongsiri 
188 
148 Towards Automatic Generation of "Preference Profiles" for Primitive 
Concept Similarity Measures on SNOMED CT 
Htet Htet Htun, Virach Sornlertlamvanich, Boontawee Suntisrivaraporn 
194 
152 ANN Approach for Predicting Economic Trends based on Electric Energy 
Consumption during Natural Disaster Period 
Akanit Kwangkaew, Virach Sornlertlamvanich, Itsuo Kumazawa, Siriya 
Skolthanarat 
200 
153 Preliminary Results on A Large-scale Cyber-Physical Hybrid Discussion 
Support Experiment 
Takayuki Ito, Takanobu Otsuka, Satoshi Kawase, Akihisa Sengoku, Shun 
Shiramatsu, Tokuro Matsuo, Tetsuya Oishi, Rieko Fujita, Naoki Fukuta, Katsuhide 
Fujita 
205 
154 Recognizing Debit Card Fraud Transaction Using CHAID and K-Nearest 
Neighbor: Indonesian Bank Case 
Indrajani, Yaya Heryadi, Lili Ayu Wulandhari, Bahtiar Saleh Abbas 
211 
 x 
162 Implement of Salary Prediction System to Improve Student Motivation using 
Data Mining Technique 
Pornthep Khongchai, Pokpong Songmuang 
217 
167 Classify Interval Range of Crime Forecasting for Crime Prevention Decision 
Making 
Astari Retnowardhani, Yaya Sudarya Triana 
223 
169 Transforming Steel Hardware Retail Distribution Network in Indonesia by 
Implementing Best Practices 
R P Manatkar, M K Tiwari 
229 
172 Detecting Online Child Grooming Conversation 
Fergyanto E. Gunawan, Livia Ashianti, Sevenpri Candra, Benfano Soewito 
235 
173 Relation between Emotional State and Speech Signal: The Case of Indonesia 
Language 
Fergyanto E. Gunawan, Kanyadian Idananta, Sevenpri Candra, Benfano Soewito 
241 
175 Search Results Side Note: Creating Information Search Context in Creative 
Design Process 
Muhammad Ikhwan Jambak, Shafiz Affendi bin Mohd Yusof 
244 
176 Internet of Things for Sleep Quality Monitoring System: A Survey 
Nico Surantha, Gede Putra Kusuma, Sani M. Isa 
249 
177 Object Instance Recognition Using Best Increasing Subsequence 
Kristopher David Harjono and Gede Putra Kusuma 
255 
178 Participatory Refinement of Participatory Outcomes: Students Iterating over 
the Design of a Mobile Learning Application 
Muhammad Mustafa Hassan, Adnan Qureshi, Andrés Moreno, Markku Tukiainen 
260 
179 Face Recognotion Using EigenFace with Naive Bayes 
Ega Bima Putranto, Poldo Andreas Situmorang, Abba Suganda Girsang 
266 
180 Increasing Accuracy of Bluetooth Low Energy for Distance Measurement 
Applications 
Benfano Soewito, Agses Yohannes Haposan Ritonga, Fergyanto E. Gunawan 
273 
181 Increasing The Accuracy of Distance Measurement Between Access Point and 
Smartphone  
Benfano Soewito, Ikhtiar Faahakhododo, Fergyanto E. Gunawan 
278 
182 Two-Way ANOVA with Interaction Approach to Compare Content Creation 
Speed Performance in Knowledge Management System 
Karto Iskandar, Noprianto, Bahtiar Saleh Abbas, Benfano Soewito, Raymondus 
Kosala 
284 
184 Voice Analysis for Detection of Deception  
Savita Sondhi, Ritu Vijay, Munna Khan, Ashok K. Salhan 
290 
185 Simple spreadsheet test case application to test spreadsheet formula in end-
user software engineering 
Noprianto, Benfano Soewito, Ford Lumban Gaol, Harco Leslie Hendric Spits 
Warnars 
297 
 xi 
186 Pangsit: spreadsheet-like domain-specific programming language for data 
entry application development 
Noprianto, Benfano Soewito 
303 
187 Knowledge Management System Development at PT. Bussan Auto Finance 
Achmad Yudho Prasetyo, Harisno 
309 
188 Design of Analytical Hierarchy Process (AHP) for Teaching and Learning 
Taufik Roni Sahroni, Hambali Ariff 
320 


Committee "Robust adaptive genetic K-Means algorithm using greedy selection for clustering"

Meyliana, Meyliana

Check 6. Learning Fraud
Detection from Big Data.pdf
by Arta Sundjaja
Submission date: 12-Sep-2019 10:57AM (UTC+0700)
Submission ID: 1102514169
File name: 6._Learning_Fraud_Detection_from_Big_Data.pdf (473.68K)
Word count: 4193
Character count: 22887





28%
SIMILARITY INDEX
11%
INTERNET SOURCES
22%
PUBLICATIONS
15%
STUDENT PAPERS
1 7%
2 3%
3 2%
4 2%
5 2%
6 2%
Check 6. Learning Fraud Detection from Big Data.pdf
ORIGINALITY REPORT
PRIMARY SOURCES
research.binus.ac.id
Internet Source
Oarabile Omaru Maruatona, Peter Vamplew,
Richard Dazeley. "Prudent Fraud Detection in
Internet Banking", 2012 Third Cybercrime and
Trustworthy Computing Workshop, 2012
Publication
Ahmad Sanmorino. "A study for DDOS attack
classification method", Journal of Physics:
Conference Series, 2019
Publication
"Modern machine learning techniques and their
applications", Electronics Communications and
Networks IV, 2015.
Publication
Submitted to Binus University International
Student Paper
ccsenet.org
Internet Source
7 1%
8 1%
9 1%
10 1%
11 1%
12 1%
13 1%
Submitted to University of Information
Technology, Yangon
Student Paper
Submitted to Sriwijaya University
Student Paper
Rajeshree Khande, Yashwant Patil. "Online
banking in India: Attacks and preventive
measures to minimize risk", International
Conference on Information Communication and
Embedded Systems (ICICES2014), 2014
Publication
Xue-Wen Chen, Xiaotong Lin. "Big Data Deep
Learning: Challenges and Perspectives", IEEE
Access, 2014
Publication
Submitted to University of Ulster
Student Paper
Submitted to CSU, Fullerton
Student Paper
Tanty Oktavia, Yanti Tjong, Harjanto Prabowo,
Meyliana. "Security and privacy challenge in
Bring Your Own Device environment: A
Systematic Literature Review", 2016
International Conference on Information
Management and Technology (ICIMTech), 2016
Publication
14 1%
15 1%
16 1%
17 1%
18 1%
Sinayobye Janvier Omar, Kiwanuka Fred,
Kaawaase Kyanda Swaib. "A state-of-the-art
review of machine learning techniques for fraud
detection research", Proceedings of the 2018
International Conference on Software
Engineering in Africa - SEiA '18, 2018
Publication
Azin Taha, David Hassan Jahed, Mohammad
Nazir Ahmad, Nor Hidayati Zakaria.
"Antecedents of customer satisfaction in mobile
commerce: A systematic literature review", 2013
International Conference on Research and
Innovation in Information Systems (ICRIIS),
2013
Publication
Muhammad Azeem Akbar, Jun Sang, Nasrullah,
Arif Ali Khan, Sajjad Mahmood, Syed Furqan
Qadri, Haibo Hu, Hong Xiang. "Success factors
influencing requirements change management
process in global software development",
Journal of Computer Languages, 2019
Publication
www.andrewleunginternationalconsultants.com
Internet Source
V. Aggelis. "Knowledge representation in our
systems", Data Mining VIII: Data, Text and Web
Mining and their Business Applications, 2007
Publication
Exclude quotes On
Exclude bibliography On
Exclude matches < 1%


Hasil Similarity "Learning Fraud Detection from Big Data in Online Banking Transactions: A Systematic Literature Review"

1,2, Harco Leslie Hendric 2, 1,

1, Harjanto Prabowo2 and 

Oktavia, Tanty

Spits Warnars, Harco Leslie Hendric

Adi, Suroto

Prabowo, Harjanto

Supangkat, Suhono Harso

Knowledge management and social learning integration: A conceptual model for higher education

Thoha, Nurianna

Reviewer Exploring Workplace Motivational Factors Perceived by Generation Y to Work as Future Talent in Companies in Indonesia (A Case Study of Undergraduate Students in Greater Jakarta Area)

Chapter 13
Multi-objective Using NSGA-2
for Enhancing the Consistency-Matrix
Abba Suganda Girsang, Sfenrianto and Jarot S. Suroso
Abstract The problem of consistencymatrix inAnalytic Hierarchy Process (AHP) is
an interesting issue. For achieving consistency in the inconsistent matrix, researchers
usually change the consistent ratio (CR) and the deviation matrix. The pursuit of a
minimal CR is important, since this index directly measures the consistency matrix in
the AHP.While the deviation matrix should be minimal such that the original opinion
of the decision makes is preserved. Ideally, the both value of CR and deviation matrix
should be minimal. However, in fact those two objectives will be conflicted if both of
them are optimized simultaneously. Therefore, a non-dominated Sorting Genetic
Algorithm-2 (NSGA-2) for solving the multi-objective problems is considered as an
appropriate approach for solving this problem. Six inconsistent AHP matrices are
successfully repaired using NSGA-2.
Keywords AHP  Consistent ratio  Genetic algorithm  NSGA-2
13.1 Introduction
In solving multi criteria decision making (MCDM), the analytic hierarchy process
(AHP) is a decision making tool to obtain a priority alternative. In AHP, a com-
parison matrix is generated to reveal the opinion of decision makers (DMs) [1–4].
One of the interesting issues of comparison matrix in AHP is the consistency. AHP
matrix can be used if it satisﬁes the consistency requirement. This issue takes
A.S. Girsang (&)
Master Information Technology, Bina Nusantara University, Jakarta, Indonesia
e-mail: agirsang@binus.edu
Sfenrianto  J.S. Suroso
Master Management Information System, Bina Nusantara University, Jakarta, Indonesia
e-mail: sfenrianto@binus.edu
J.S. Suroso
e-mail: jsembodo@binus.edu
© Springer Science+Business Media Singapore 2016
F. Pasila et al. (eds.), Proceedings of Second International Conference
on Electrical Systems, Technology and Information 2015 (ICESTI 2015),
Lecture Notes in Electrical Engineering 365, DOI 10.1007/978-981-287-988-2_13
123
attention for researchers to alleviate the inconsistency of pairwise comparison
preference.
In a single objective problem, some researches use metaheuristic algorithm to
solve the inconsistent matrix problem. Lin et al. [5] used genetic algorithm to solve
the inconsistent in the comparison matrix. Yang et al. [6] continues this research to
solve the inconsistent comparison matrix by combining particle swarm optimization
PSO and Taguchi method. Girsang et al. [3, 4] used ant colony optimization to
solve the inconsistent matrix. Although those metaheuristic methods are success-
fully solved that problem, the variations of the implemented metaheuristic, is rarely
achieved. Girsang et al. [7] implemented multi-objective approach using PSO to
solve this problem. Implementing NSGA-2 as one of the algorithm using genetic
algorithm is interesting research in this area.
13.2 Multi-objective for Repairing the Inconsistent Matrix
The main purpose of multi-objective algorithm is to ﬁnd a set of solutions which
optimally balances the weakness among objectives of a multi-objective problems
[8]. This problem uses two functions objective (F1 and F2) to point out each
objective. The ﬁrst objective is to minimize CR as shown Eq. (13.1). In order to
obtain the consistent comparison matrix, the threshold CR must be less than 0.1.
Min F1 ¼ CR
0\F1\ 0:1:
ð13:1Þ
Saaty [1] deﬁned the threshold of CR is 0.1. Suppose the CR is deﬁned in
Eqs. (13.2)–(13.4).
AW ¼ kmax W ; ð13:2Þ
CI ¼ kmax  nð Þ= n 1ð Þ; ð13:3Þ
CR ¼ CI=RI; ð13:4Þ
where A is a comparison matrix, λmax is the largest eigenvalue, W is the eigenvector
of the matrix.
Further, CI is consistent index, n represents number criteria or size matrix, and
RI (random consistency index) is the average index of randomly generated weights.
Value RI on each size matrices can be described on Table 13.1. Eigenvector W can
be represented as a set of the weight of each criteria of matrix A, while λ can be
represented as a set of scalar corresponding to eigenvector W as Eq. (13.2).
124 A.S. Girsang et al.
The second objective is to minimize deviation matrix as shown Eq. (13.5). Di is
used to deﬁne the deviation matrix.
MinF2 ¼ Di ð13:5Þ
Difference index (Di) is deﬁned as the real difference between the same gene
values in two genotypes. Di is deﬁned in Eq. (13.6).
Di ¼
PðG0:=GÞþ ðG:=G0Þ
n2  1  1; ð13:6Þ
where Gʹ and G are row vectors comprising the lower triangular elements of the
substitute matrix Aʹ and of the original matrix A, respectively; “./” means
element-to-element division. Smaller Di indicates more similar between two
matrices. Di will be 0 if two matrices are same.
13.3 Implement Algorithm NSGA-2 for Repairing
the Matrix
The encoding of matrix can be assembled by picking all elements in the matrix.
However, due to the elements of multiplicative preference matrix have a relation
such that aij = 1/aji; aii = 1, an encoding node can only encodes the lower triangular
elements of the matrix as nodes. It can be encoded by picking row by row
sequentially in the elements of the lower triangular matrix. Take matrix A (n = 4) for
example and its encoding shown in Fig. 13.1. The number element of encode A can
be determined (n2−n/2). All elements in the inconsistent matrix should be changed
into new value to achieve the consistent matrix. The new values are generated by
fractioned from original values into several candidate values. This strategy makes
that although fraction data changes the origin value, it only changes the weight of
judgment without changing the tendency judgment.
Table 13.1 Random consistency index (RI)
Number
criteria
1 2 3 4 5 6 7 8 9 10 11
RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49 1.51
A =
⎥⎥
⎥⎥
⎦
⎤
⎢⎢
⎢⎢
⎣
⎡
1
4/1 3/1 1 8/1
5
5 83
1 3 5/1
1 5/1 4 3/1
Encode A = 5-1/4-1/3-3-5-8
Fig. 13.1 Matrix A with n = 4 and its encoding
13 Multi-objective Using NSGA-2 for Enhancing the Consistency-Matrix 125
Second, if the origin element is 1 (neutral), the data should not be fractioned. It is
performed to maintain the neutrality of judgment. The third, each candidate value is
built from minimal to maximal based on the interval of fraction factor (ψ). The
different value ψ will set the different number of candidate values. For example, if
ψ = 0.2 then number candidate value of each element will be 40 ¼ 910:2
 
. Suppose
gr is the origin value on node r and n is the size matrix, thus GB can be described in
Eq. (13.7)
GB ¼ g1  g2  g3; . . .; gn2n
2
ð13:7Þ
Each elements of gr is fractioned into several candidate element grs. Value
s indicates the index of candidate element. The candidate element can be produced
by following the role in Eq. (13.8).
grs ¼
grs1þw; if 1\gr  9
1
1
grs1 þw
; if 1=9 gr\1
8><
>:
ð13:8Þ
There are some steps to implement NSGA-2 to repair the inconsistent matrix.
(a) Generating the populations. There are 100 populations in each generations.
Each population consists of chromosomes which represent the candidate
elements.
The candidate elements are came from fractioning the original element. In this
ﬁrst step, the candidate element is chosen randomly. Figure 13.2 shows one
sample chromosome of encoding matrix A on Fig. 13.1.
(b) Generated populations are sorted based on non-dominated which depicts the
level front. The ﬁrst front dominates the second front, and the second front
dominates the third front, and so forth.
(c) Each chromosomes as representing solution has crowding distance. Crowding
distance states the distribution solution in its front. The longer crowding
distance is more distribution solution. Therefore, the longer crowding distance
is better.
(d) To generate the parent individual, process “the tournament based selection” is
used. In this process the parent is chosen as the best ﬁtness. The better ﬁtness
is based on the small front and the long crowding distance. If there are two
solutions have the different rank front, the small rank front is chosen, however
if they have the same rank front, the longer crowded distance is chosen.
(e) Crossover is performed in process of generating two parents, PCr = 0.9
(f) Mutation is performed, PMu = 0.1
Chromosome1 4.8 1/4.8 1/ 2.2 5.2 4.8 6.4
Fig. 13.2 Chromosome1 as sample chromosome of Fig. 13.1
126 A.S. Girsang et al.
T
ab
le
13
.2
T
he
in
co
ns
is
te
nc
y
m
at
ri
ce
s
M
at
ri
x
V
al
ue
el
em
en
t
of
in
co
ns
is
te
nt
m
at
ri
x
C
R
A
1
5

5

1 5
0.
25
4
A
2
9

1 3

1 5

5

1 2

2
0.
17
2
A
3
3

1 2

1 7

6

9

9

2

4

4

1 5
0.
13
9
A
4
1 2

9

3

1

4

1 5

5

5

5

7

7

3

3

1 3

3

1 7
0.
13
6
A
5
7

3

9

1

2

1 5

1 2

3

1 4

2

3

4

1 3

3

9

1 3

1 2

1 7

1 3

1 4

1 5
0.
21
A
6
1 5

1 3

3
1 7

1 5

1 6

1 6

1 3

1 3
3

1 6

1 3

1 4

4

2

3

5

1 6
7

5

5

4

7

5

8

6

6

2
0.
16
9
[9
]
13 Multi-objective Using NSGA-2 for Enhancing the Consistency-Matrix 127
T
ab
le
13
.3
M
at
ri
ce
s
re
pa
ir
ed
w
ith
th
e
be
st
C
R
an
d
D
i
M
at
ri
x
B
es
t
C
R
B
es
t
D
i
E
le
m
en
ts
of
lo
w
er
tr
ia
ng
ul
ar
m
at
ri
x
C
R
D
i
E
le
m
en
ts
of
lo
w
er
tr
ia
ng
ul
ar
m
at
ri
x
C
R
D
i
A
1
0-
4.
4-
0.
62
5
3.
1.
10
−
6
0.
26
2
5.
8-
4.
0-
0.
25
0.
09
9
0.
14
6
A
2
8-
0.
62
5-
0.
13
2-
2.
4-
0.
45
5-
3.
8
3.
10
−
4
0.
16
6
7.
8-
0.
35
7-
0.
15
6-
4.
2-
0.
38
5-
2.
4
0.
09
9
0.
01
8
A
3
0.
71
4-
0.
22
7-
5.
8-
2.
0-
8.
8-
5.
6-
2.
4-
8.
6-
0.
83
3
0.
00
3
0.
46
4
3.
4-
0.
5-
0.
2-
5.
8-
7.
4-
9.
0-
1.
6-
2.
8-
5.
6-
0.
26
3
0.
08
8
0.
02
7
A
4
71
4-
7.
0-
8.
8-
1.
0-
1.
8-
0.
20
8-
8.
8-
8.
2-
1.
6-
7.
0-
1.
2-
2.
0-
0.
14
7-
1.
2-
0.
12
2
0.
00
9
0.
22
8
0.
5-
7.
8-
3.
4-
1.
0-
4.
6-
0.
31
2-
7.
6-
6.
2-
5.
0-
6.
8-
2.
4-
3.
8-
0.
31
2-
2.
0-
0.
17
2
0.
09
5
0.
02
6
A
5
2-
6.
2-
7.
8-
1.
0-
2.
6-
0.
29
4-
0.
83
3-
2.
8-
0.
23
8-
1.
2-
4.
0-
5.
4-
0.
5-
2.
8-
4.
6-
0.
45
5-
0.
71
4-
0.
2-
0.
5-
0.
71
4-
0.
15
2
0.
03
2
0.
18
9
3.
0-
4.
6-
4.
6-
1.
0-
2.
0-
0.
25
-0
.8
33
-2
.8
-0
.1
56
-
2.
0-
4.
4-
4.
4-
0.
26
3-
2.
6-
3.
4-
0.
41
7-
0.
33
3-
0.
13
2-
0.
45
5-
0.
22
7-
0.
29
4
0.
08
9
0.
08
8
A
6
38
5-
0.
83
3-
2.
4-
0.
13
2-
0.
13
5-
0.
11
6-
0.
22
7-
0.
33
3-
0.
21
7-
1.
2-
0.
23
8-
0.
62
5-
0.
23
8-
4.
6-
2.
8-
1.
2-
1.
2-
0.
55
6-
7.
6-
9.
0-
2.
0-
1.
6-
8.
4-
1.
4-
7.
4-
8.
6-
4.
6-
2.
4
0.
04
3
0.
22
8
0.
2-
0.
62
5-
2.
8-
0.
25
-0
.2
17
-0
.1
19
-0
.2
08
-
0.
45
5-
0.
31
2-
3.
8-
0.
25
-0
.7
14
-0
.2
63
-5
.2
-
2.
0-
1.
2-
6.
8-
0.
62
5-
7.
4-
4.
6-
4.
6-
3.
0-
6.
6-
8.
6-
8.
8-
7.
6-
3.
0-
2.
2
0.
09
0
0.
10
5
128 A.S. Girsang et al.
13.4 Experimental Results
To see the performance of NSGA-2 in repairing some inconsistent matrix, there are
six inconsistent matrices which need to be modiﬁed as shown in Table 13.2. When
the CR is lowest (good consistent ratio), it leads to the highest (the worst) deviation,
and vice versa, as seen on Table 13.3.
However, in order to get the acceptable matrix, the CR of modiﬁed matrix is
limited below 0.1. It makes the solution consist of some relations “CR-deviation”
which can be identiﬁed as non-dominated solutions.
13.5 Conclusion
This paper presents a study to use the NSGA-2, multi-objective algorithm using
GA, to solve the inconsistent problem on comparison matrix in AHP. There are two
objectives (consistent ratio and deviation matrix) considered in rectifying the matrix
in order to be consistent. However they are conflicting in that process. This algo-
rithm offers some non-dominated solutions which are also satisﬁed the acceptable
consistent matrices. To see the performance, six inconsistent comparison matrices
with varying size are repaired by the NSGA-2. Besides repairing inconsistent
comparison matrices, the NSGA-2 also can generate some non-dominated solution
which can be classiﬁed as optimal solutions.
References
1. Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill, New York (1980)
2. Saaty, T.L., Decision Making for Leaders, vol. 7, Esf Editeur (1984)
3. Girsang, A.S., Tsai, C.-W., Yang, C.-S.: Ant algorithm for modifying an inconsistent pairwise
weighting matrix in an analytic hierarchy process. Neural Comput. Appl., 1–15 (2014)
4. Girsang, A.S., Tsai, C.-W., Yang, C.-S., Ant Colony Optimization for Reducing the
Consistency Ratio in Comparison Matrix. In: International Conference on Advances in
Engineering and Technology (ICAET’2014) Singapore (2014)
5. Lin, C.C., Wang, W.C., Yu, W.D.: Improving AHP for construction with an adaptive AHP
approach (A3). Autom. Constr. 17, 180–187 (2008)
6. Yang, I., Wang, W.C., Yang, T.I.: Automatic repair of inconsistent pairwise weighting matrices
in analytic hierarchy process. Autom. Constr. 22, 290–297 (2012)
7. Girsang, A.S., Tsai, C.-W., Yang, C.-S., Multi-objective particle swarm optimization for
repairing inconsistent comparison matrices. Int. J. Comput. Appl. 36 (2014)
8. Engelbrecht, A.P., Fundamentals of Computational Swarm Intelligence. Wiley (2006)
9. Saaty, T.L., Vargas, L.G., Models, Methods, Concepts & Applications Of The Analytic
Hierarchy Process. Kluwer (2001)
13 Multi-objective Using NSGA-2 for Enhancing the Consistency-Matrix 129


Multi-objective using NSGA-2 for enhancing the consistency-matrix

Heriyati, Pantri

Assessment of Companies’ Peripheral Vision Capability - The Case of the Indonesian Manufacturing Industry

Form Reviewer "Assessment of Companies’ Peripheral Vision Capability - The Case of the Indonesian Manufacturing Industry"

Sasmoko, Sasmoko

Check JJA Prof Sasmoko _
Scopus 2016 - 3 - Diagnostic
Evaluation.pdf
by Arta Moro Sundjaja
Submission date: 09-Aug-2018 06:07PM (UTC+0700)
Submission ID: 624024728
File name: JJA_Prof _Sasmoko___Scopus_2016_-_3_-_Diagnostic_Evaluation.pdf  (811.67K)
Word count: 5255
Character count: 30691












14%
SIMILARITY INDEX
13%
INTERNET SOURCES
7%
PUBLICATIONS
4%
STUDENT PAPERS
1 8%
2 2%
3 1%
4 1%
5 1%
Check JJA Prof Sasmoko _ Scopus 2016 - 3 - Diagnostic
Evaluation.pdf
ORIGINALITY REPORT
PRIMARY SOURCES
research.binus.ac.id
Internet  Source
irep.iium.edu.my
Internet  Source
www.aspbs.com
Internet  Source
Sigit Wijaksono, Sasmoko, Y Indrianti, SA
Widhoyoko. "Jakarta socio-cultural ecology: a
sustainable architecture concept in urban
neighbourhood", IOP Conference Series: Earth
and Environmental Science, 2017
Publicat ion
Nooshin Mohebbi, Faezeh Akhlaghi,
Mohammad Hossein Yarmohammadian,
Masumeh Khoshgam. "Application of CIPP
model for evaluating the medical records
education course at master of science level at
Iranian medical sciences universities", Procedia
- Social and Behavioral Sciences, 2011
6 1%
7 1%
Exclude quotes On
Exclude bibliography On
Exclude matches < 1%
Publicat ion
Pragya Gupta, Neeraj Kaushik. "Dimensions of
service quality in higher education – critical
review (students’ perspective)", International
Journal of Educational Management, 2018
Publicat ion
Ahmad Ali Foroughi Abari, Mohammad
Hossein Yarmohammadian, Mina Esteki.
"Assessment of quality of education a non-
governmental university via SERVQUAL
model", Procedia - Social and Behavioral
Sciences, 2011
Publicat ion


Similarity "Diagnostic evaluation of lecturer quality in learning process at “new private higher education institutions” in Tangerang, Indonesia"

Form Reviewer "Diagnostic evaluation of lecturer quality in learning process at “new private higher education institutions” in Tangerang, Indonesia"

Binus University

Not a member yet