Binus University Repository

CANDRA, SEVENPRI

Form Reviewer "A Study of Comparison of Feature Extraction Methods for Handwriting Recognition"

Nugroho Perwiro Atmojo, S.Kom., M.M.S.I, Robertus

Mahesa, Restu

Wandoko, Wanda

Utami Tjhin, Viany

Prabowo, Haijanto

Budiastuti, Dyah

Gaol, Ford L.

Koeswidi Astuti, Theresia Nanin

Form Reviewer "Research plan development concerning e-image impact towards online purchase intention and premium pricing strategies in Indonesia community based online market"

Hamali, Sambudi

Nur Suci, Rumondang Puji

Utami, Ami Fitri

Hanisman, Hanisman

Arga, Filia

978-1-5090-3352-2/16/$31.00 ©2016 IEEE 16-18 November 2016, Aston Tropicana Hotel, Bandung, Indonesia 
2016 International Conference on Information Management and Technology (ICIMTech) 
Page 329 
 
 
Using Analytic Hierarchy Process and Decision Tree 
for a Production Decision Making 
 
Sambudi Hamali, Rumondang Puji Nur Suci, Ami Fitri 
Utami, Hanisman 
School of Business and Management 
Bina Nusantara University 
Jakarta, Indonesia 
shamali@binus.edu, rsitorus@binus.edu, 
autami@binus.edu, hanismanlim@gmail.com 
 
Filia Arga 
PhD Candidate in Economics 
University of Illinois at Urbana 
Champaign, Illinois, United States 
arga2@illinois.edu 
Abstract—Today, the light weight bricks are on high demand 
in the market, where “X” is considering to expand its’ business 
to produce this product. However, expanding business to a new 
business lines requires a comprehensive considerations. 
Complexity of decision-making not only lies in the uncertainty 
or the imperfections of information, but involves many criteria 
and sub-criteria used to rank the alternatives of a decision. The 
purpose of this study is to understand the best solution on 
whether or not the light weight bricks being produced by “X”. 
Hence, this study applied the analytic hierarchy process (AHP) 
and Decision Tree Analysis (DTA) as the rational decision 
making tools may help “X” to decide whether or not to invest 
in the new business line. The results based on data processing 
AHP of directors and general managers indicate that the "X" 
has a tendency to decide to make the light weight bricks. While 
based on the DTA shows that the "X" will benefit if it decides 
to make the light weight bricks. 
Keywords—Multi-criteria, Expected Monetary Value, 
Analytic Hierarchy Process, Decision Tree Analysis, Light 
weight bricks 
I. INTRODUCTION 
In today’s era, the massive development of constructions 
are leveraging the needs of its’ basic materials which boost 
the consumptions. In this case, one of the materials which 
most currently used is the white light weight brick which 
has a smooth and well-flattened surface. This light weight 
bricks were invented for various aims includes lightened the 
structure’s load of a construction, fasten the development 
execution, and minimize the material residue which often 
occur on the wall installation process. 
 “X” is a company which runs its’ business in producing 
and selling concrete block as the product. Understanding the 
fact that the light weight bricks are on high demand in the 
market, “X” is now considering to expand its’ business to 
produce the product. However, expanding business to a new 
business lines requires a comprehensive considerations. 
Especially in this case, adding the light weight bricks as 
their new product line will also load the company with a 
huge capital investment includes new machine, new land, 
and new suppliers. 
Based on previous discussions, the use of Analytic 
Hierarchy Process (AHP) and Decision Tree Analysis 
(DTA) as the rational decision making tools may help “X” 
to decide whether or not to invest in the new business line. 
AHP as one of a basic decision making tools is developed 
by Thomas L. Saaty, one of professor in the Wharton 
School of Economics (1971-1975).  The AHP itself 
designed to encounter both the rational and intuitive notions 
to choose the best alternatives above all [1].  On the other 
hands, the DTA is a form of graphical decision process 
which indicates various factors includes the decision 
alternatives, the natural conditions and possibilities, and the 
payoffs for each alternatives combinations and natural 
conditions [2].  In DTA, the expected monetary value 
(EMV) being used to measures the attractiveness of 
alternatives. AHP and DTA has been used in various 
settings to make decisions [3,4,5,6]. 
This study aimed to understand the best solution on 
whether or not the light weight bricks being produced by 
“X” by using AHP and DTA as the analytical tools. 
Moreover, this paper composed of several parts includes the 
literature review of the AHP and DTA, the methodology on 
how this study use AHP and DTA as analytical tools, and 
the conclusion about what is the best solutions based on the 
tools. 
 
II. THEORETICAL REVIEW 
A. Analytic Hierarchy Process (AHP) 
Analytical Hierarchy Process is a decision analysis 
method with a compound criteria that used to lower the ratio 
of the paired comparisons of criteria and alternatives, both 
discrete and continuous that will arranged in a multilevel 
hierarchy. This comparison can be drawn from the actual 
measurements or using the basic scale that shows interest / 
relative strength based on the preferences of participants 
[1]. 
There are four steps in AHP method that will explain as 
follows [7] : 
1)  Developing hierarchy: In order to create the 
hierarchy structure, we need to decide the main goals. The 
main objective will be structured as a top-level hierarchy. 
The criteria that matched to assess our alternatives will be 
978-1-5090-3352-2/16/$31.00 ©2016 IEEE 16-18 November 2016, Aston Tropicana Hotel, Bandung, Indonesia 
2016 International Conference on Information Management and Technology (ICIMTech) 
Page 330 
 
 
structured below the top level. Each criterion has a different 
intensity. The last level of a hierarchy is called sub-criteria 
(if it may be required). 
2) Making the pairwise comparison matrix. This method 
aimed to set the priorities that depicts the relative 
contribution or influence of each element against the 
objectives and criteria on the previous level. Pairwise 
matrix made by comparing each pair of alternatives against 
the criteria tested. In the assessment of the relative 
importance of the two elements applied reciprocal axioms, 
meaning that if the elements of a rated three times more 
important than b, then the element b must be equal to 1/3 
times the importance compared to the a element. In 
addition, comparison of the two elements that are the same 
will generate a number 1, meaning equally important. Two 
different elements can be assessed equally important. If 
there are n elements, it will obtain the pairwise comparison 
matrix of size n x n. The number of scoring required in 
preparing this matrix is n (n-1) / 2 due to reciprocal matrix 
and diagonal elements equal to 1. 
3) Synthesizing. This method aimed to Make a synthesis 
to produce a single number that indicates the priority of 
each element 
4) Logical Consistency Tests. In this stage, we try to 
determine whether the provision in the comparison between 
the value of the object has been done consistently. 
Consistency ratio (CR) indicates to which extent the 
analysis is consistent in delivering value to the comparison 
matrix. In general the results of the analysis to be 
considered as consistent is when the rate of CR ≤ 10%. If 
CR> 10% it is necessary to conduct a re-evaluation 
consideration in preparing the comparison matrix. 
Furthermore, below are the scale priorities and the example 
of pairwise comparison matrix based on [7]. 
 
TABLE I. SCALE PRIORITIES 
Intensity of 
importance 
Definition 
1 
3 
5 
7 
9 
2,4,6,8 
 
Both elements are important. 
One element slightly favor over another. 
One element strongly favor over another. 
One element favored very strongly over 
another. 
One element favored absolute over another. 
The value of two elements when compared 
each other would be too small. 
Source: Harjanto (2009) 
 
 
 
TABLE II. THE EXAMPLE OF PAIRWISE COMPARISON MATRIX 
Criteria A B C 
A 1 o o 
B x 1 o 
C x o 1 
Source: Harjanto (2009) 
 
B. The Decision Tree 
The decision tree (decision tree) is a graphical display 
indicating the decision process of alternative decisions, 
natural conditions and opportunities, and payoffs for each 
combination of alternative decisions and natural conditions 
[2]. 
There are five steps to analyze problems using a decision 
tree [2]. 
1) Define the problem. 
2) Draw a decision tree. 
3) Determine opportunities for natural conditions. 
4) Estimate of payoffs. 
This step aimed to estimate the payoffs for each 
combination of alternative decisions and natural 
conditions as possible. 
5) Solving problem.  
The step tries to solve the problem by counting the 
EMV for each point in the natural condition. It can be 
done by working from back to front (backward), 
which expands from the right side of the trees, 
continue to get to a decision point on the left. 
 
III. METHODOLOGY 
This section  presents  the  case  of  decision making  
carried  out  at  the “X”. This study use descriptive survey 
as its’ method where both primary and secondary data are 
being gathered. The primary data were gathered by 
interviewing & spreading questionnaires to managers and 
directors of “X”.  On the other hands, the secondary data 
were gathered by analysing company’s report, histories, and 
the previous research.  
Furthermore, the detailed methods of this study may 
inferred as below steps: 
1)  Determining Criteria:  This process implemented by 
discussing & interviewing what criteria are needed by the 
company on deciding whether to or not to produce a new 
product. In this case, the discussions & interviews were ran 
with the company’s stakeholders include managers and 
directors. Based on the interviews and discussions there are 
four main criteria found includes cost, location, basic 
material, and management. After the criteria were extracted, 
the hierarchy of criteria being developed in order to make 
the decision. The criteria hierarchy are able to see in below 
fig.1 : 
 
 
Fig. 1. Production Hierarchy of Light Weight Bricks 
978-1-5090-3352-2/16/$31.00 ©2016 IEEE 16-18 November 2016, Aston Tropicana Hotel, Bandung, Indonesia 
2016 International Conference on Information Management and Technology (ICIMTech) 
Page 331 
 
 
 
2)  Developing & spreading questionnaire, The 
questionnaire development process implemented based on 
the pairwise comparison in order to understand the load of 
each criteria and alternative. Furthermore, the 
questionnaires were spread to the decision makers. 
3)  Calculating the loads of each criterion & Making 
decision. This process aimed to calculates the loads of each 
criteria in the AHP frames. In this case, the results of 
calculation will be differed among each decision maker. 
Thus, different load are given to both managers and 
directors with 70% and 30% in sequent 
4)  Estimating cost for each alternative 
5)  Measuring probability and the failure levels of each 
alternatives 
6)  Structuring decision tree 
7)  Deriving the Expected Monetary Value 
8)  Selecting the best option 
 
IV. RESULTS & DISCUSSION 
A. AHP Results 
After the questionnaires are submitted, the result of the 
questionnaires will be described into the Pairwise 
Comparison Matrix as follows: 
 
TABLE III. PAIRWISE COMPARISSON MATRIKS FOR THE CRITERIA FROM 
THE DIRECTOR 
 Budget Location 
Raw 
Material Management 
Budget 1 2 3 5 
Location 0.5 1 0.5 2 
Raw 
Material 0.33 2 1 2 
Management 0.2 0.5 0.5 1 
Total 2.03 5.5 5 10 
Source: Data Analysis Result, 2016 
 
TABLE IV. PAIRWISE COMPARISSON MATRIKS OF THE CRITERIA FROM 
THE GENERAL MANAGER 
 Budget Location 
Raw 
Material Management 
Budget 1 2 4 4 
Location 0.5 1 0.5 2 
Raw 
Material 0.25 2 1 2 
Management 0.25 0.5 0.5 1 
Total 2 5.5 6 9 
Source: Data Analysis Result, 2016 
 
TABLE V.  FINAL ASSESSMENT RESULT FROM THE DIRECTOR 
Criteria Weight Produce Not To Produce 
Budget 0.493 0.37* 0.12 
Location 0.179 0.12 0.06 
Raw Material 0.23 0.19 0.04 
Management 0.097 0.06 0.03 
Total  0.75 0.25 
Source: Data Analysis Result, 2016 
 
 
TABLE VI. FINAL ASSESSMENT RESULT FROM THE GENERAL MANAGER 
Criteria Weight Produce Not To Produce 
Budget 0.507 0.41 0.10 
Location 0.177 0.13 0.04 
Raw 
Material 0.214 0.17 0.04 
Management 0.101 0.08 0.03 
Total  0.79 0.21 
Source: Data Analysis Result, 2016 
 
The following step is to multiply the total of each final 
assessment with the value of the decision maker. The value 
of the decision maker is decided by a thorough discussion 
between the Director and the General Manager. The result 
can be visualized in the next table.  
 
TABEL VII. FINAL CALCULATION 
 
Weight of 
decision 
maker 
Produce Not To 
Produce 
Director 0.70 0.525a 0.175 
General 
Manager 0.30 0.237 0.063 
Total 1.00 0.762 0.238 
Source: Data Analysis Result, 2016 
a As a result from weight of decision maker x Weight of each decision 
= 0.70 x 0.75 = 0.525 
Based on the result gained by AHP Data analysis from 
the director and the general manager, there are two results 
of the calculation; 0.763 for producing and 0.238 for not 
producing. This shows that “X” has the tendency to produce 
the white lightweight concrete.  
 
B. The Decision Tree Results 
Next, is the probability data and the value of the decision 
for the available alternatives based on the interview with the 
director of “X”. The director has been in the construction 
business since 1997 as a director and he has also gathered 
knowledge based on the information from his colleagues in 
the lightweight concrete business.  
 
TABEL VIII. PROBABILITY DATA AND DECISION RESULT OF DECISION 
TREE 
No Criteria Probabilities for natural 
condition 
Decision Result  
(In Million Rupiah) 
Good 
Econo
my 
Poor 
Econo
my 
Norm
al 
Econo
my 
Good 
Econo
my 
Poor 
Econo
my 
Norm
al 
Econ
omy 
1. To 
Produce 
0.4 0.1 0.5 500 -100 200 
2 Not To 
Produce 
0 0 0 0 0 0 
Source: Data Analysis Result, 2016 
978-1-5090-3352-2/16/$31.00 ©2016 IEEE 16-18 November 2016, Aston Tropicana Hotel, Bandung, Indonesia 
2016 International Conference on Information Management and Technology (ICIMTech) 
Page 332 
 
 
From Table VIII we shall continue to the decision tree as 
follows, 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig. 2. Decision Tree 
 
After the decision tree is made, the calculation is 
conducted by looking at the odds and the result set by the 
director of “X” based on each branch; to produce and not to 
produce.  
EMV calculation: 
EMV (to produce) = (0.4 x 500) + (0.1 x -100) + (0.5 x 
200) 
= 200 + (-10) + 100 = 290 million 
EMV (not to produce) = 0  
 
From the result of the calculation above, “X” will gain 
profit if they decide to produce the white lightweight 
concrete.  
 
V. CONCLUSIONS 
As a whole, this paper elaborates an AHP and DTA-
based methodology and measure to understand the best 
solution on whether or not the light weight bricks being 
produced by “X”. The results suggest that based on data 
processing AHP of directors and general managers indicate 
that the "X" has a tendency to decide to make white light 
brick. While based on the DTA shows that the "X" will 
benefit if it decides to make white light brick. However, this 
study could not be generalized because the study to a 
decision to produce or not the light weight bricks. 
 
 
REFERENCES 
[1] T. L. Saaty and L. G. Vargas, Models, Methods, Concepts & 
Applications of the Analytic Hierarchy Process, International Series 
in Operations Research & Management Science 175, Springer 
Science+Business Media New York, 2012. 
[2] J. Heizer & B. Render, “Operation Management,”, 10th ed., New 
Jersey, 2011. 
[3] L-A. Vidal, F. Marle, and J-C. Bocquet. "Using a Delphi process and 
the Analytic Hierarchy Process (AHP) to evaluate the complexity of 
projects." Expert systems with applications 38.5, pp. 5388-5405, 
2011. 
[4] PK. Dey, "Project risk management: a combined analytic hierarchy 
process and decision tree approach." Cost Engineering, 44.3, pp. 13-
27, 2002. 
[5] E. Battistoni, AF. Colladon, L. Scarabotti, MM. Schiraldi, "Analytic 
hierarchy process for new product development." International 
Journal of Engineering Business Management 5 (2013). 
[6] Liao, Wen-Hwa, and Wan-Li Qiu. "Applying analytic hierarchy 
process to assess healthcare-oriented cloud computing service 
systems." SpringerPlus5.1 (2016): 1030. 
[7] E. Harjanto, “Manajemen Operasi,”, Cetakan Ketujuh, Jakarta, 2008. 
 
 
To Produce 
(290 million) 
Not To 
Produce 
(0) 
0 
500 million
Good 
Economy (0,4) 
200 millionNormal Economy (0,5) 
- 100 million
Poor Economy 
(0,1) 


English

Paper "Using analytic hierarchy process and Decision Tree for a production decision making"

Ranny, Ranny

(Check) Voice Recognition using
k Nearest Neighbor and Double
Distance Method
by Rini Wongso
Submission date: 07-Jan-2021 11:52AM (UTC+0700)
Submission ID: 1483972713
File name: ognition_using_k_Nearest_Neighbor_and_Double_Distance_Method.pdf (426.03K)
Word count: 2958
Character count: 14512





15%
SIMILARITY INDEX
10%
INTERNET SOURCES
4%
PUBLICATIONS
10%
STUDENT PAPERS
1 6%
2 5%
3 3%
4 1%
Exclude quotes On
Exclude bibliography On
Exclude matches < 1%
(Check) Voice Recognition using k Nearest Neighbor and Double
Distance Method
ORIGINALITY REPORT
PRIMARY SOURCES
Submitted to Universiti Malaysia Perlis
Student Paper
reports.ias.ac.in
Internet Source
Xing Bao. "Transportation Disruptions in Supply
Chain", 2007 International Conference on
Wireless Communications Networking and
Mobile Computing, 09/2007
Publication
www.ijetae.com
Internet Source
FINAL GRADE
/0
(Check) Voice Recognition using k Nearest Neighbor and Double
Distance Method
GRADEMARK REPORT
GENERAL COMMENTS
Instructor
PAGE 1
PAGE 2
PAGE 3
PAGE 4
PAGE 5


Check Similarity "Voice Recognition Using k Nearest Neighbor and Double Distance Method"

Sanjaya, S.IP., M.Si., Marta

LEMBAR 
HASIL PENILAIAN SEJAWAT SEBIDANG ATAU PEER REVIEW 
KARYA ILMIAH : Prosiding 
 
 
 
Judul Makalah : The Framing of Women Figure in Indonesian Political Stage 
Penulis Makalah : Marta Sanjaya, S.IP., M.Si. 
Identitas Makalah : a. Judul Prosiding  : Proceeding International Conference on Transformation in 
Communication (ICOTIC) 2016 
b. ISBN : 2502-3500 
c. Tahun Terbit : 2016 
d. Penerbit : Universitas Telkom Bandung 
e. Jumlah Halaman : 27
Kategori Publikasi Makalah : Prosiding Forum Ilmiah Internasional 
Prosiding Forum Ilmiah Nasional 
 
Hasil Penilaian Peer Review : 
 
Komponen yang Dinilai 
Nilai Maksimal Prosiding 3.00 Nilai Akhir 
Yang 
Diperoleh 
Internasional 
 
Nasional 
 
 
a. Kelengkapan unsur isi buku (10%) 0.3 
 
0.24 
b. Ruang lingkup dan kedalaman 
pembahasan (30%) 
0.9 
 
0.72 
c. Kecukupan dan kemutahiran 
data/informasi dan metodologi 
(30%) 
0.9 
 
0.72 
d. Kelengkapan unsur dan kualitas 
penerbit (30%) 0.9 
 
0.72 
Total (100%) 3 
 
2.4 
 
 
Jakarta, 15-12-2020 
 
 
 
 
 
Dr. Muhamad Aras, S. Pd., M.Si.  
NIP D5215 
Unit Kerja: Universitas Bina Nusantara 
09 1-2020
LEMBAR 
HASIL PENILAIAN SEJAWAT SEBIDANG ATAU PEER REVIEW 
KARYA ILMIAH : Prosiding 
 
 
 
Judul Makalah : The Framing of Women Figure in Indonesian Political Stage 
Penulis Makalah : Marta Sanjaya, S.IP., M.Si. 
Identitas Makalah : a. Judul Prosiding  : Proceeding International Conference on Transformation in 
Communication (ICOTIC) 2016 
b. ISBN : 2502-3500 
c. Tahun Terbit : 2016 
d. Penerbit : Universitas Telkom Bandung 
e. Jumlah Halaman : 27
Kategori Publikasi Makalah : Prosiding Forum Ilmiah Internasional 
Prosiding Forum Ilmiah Nasional 
 
Hasil Penilaian Peer Review : 
 
Komponen yang Dinilai 
Nilai Maksimal Prosiding 3.00 Nilai Akhir 
Yang 
Diperoleh 
Internasional 
 
Nasional 
 
 
a. Kelengkapan unsur isi buku (10%) 0.3 
 
0.255 
b. Ruang lingkup dan kedalaman 
pembahasan (30%) 
0.9 
 
0.765 
c. Kecukupan dan kemutahiran 
data/informasi dan metodologi 
(30%) 
0.9 
 
0.765 
d. Kelengkapan unsur dan kualitas 
penerbit (30%) 0.9 
 
0.765 
Total (100%) 3  2.55 
 
 
Jakarta, 15-12-2020 
 
 
 
 
 
Mia Angeline, S.Kom., M.M., M.I.Kom 
NIP D3210 
Unit Kerja: Universitas Bina Nusantara 
 
09 1-2020


Form Reviewer "The Framing of Women Figure in Indonesian Political Stage"

(Check) The Framing of Women
Figure in Indonesian Political
Stage
by Rini Wongso
Submission date: 21-Jan-2021 09:34AM (UTC+0700)
Submission ID: 1491164500
File name: The_Framing_of_Women_Figure.pdf (378.95K)
Word count: 7242
Character count: 41473





























5%
SIMILARITY INDEX
6%
INTERNET SOURCES
4%
PUBLICATIONS
2%
STUDENT PAPERS
1 1%
2 1%
3 1%
4 1%
5 1%
6 1%
Exclude quotes Off
Exclude bibliography On
Exclude matches < 1%
(Check) The Framing of Women Figure in Indonesian Political
Stage
ORIGINALITY REPORT
PRIMARY SOURCES
www.tandfonline.com
Internet Source
asia-pacific-solidarity.net
Internet Source
www.transparency.org
Internet Source
journals.sagepub.com
Internet Source
www.researchgate.net
Internet Source
jnk.phb.ac.id
Internet Source
FINAL GRADE
/0
(Check) The Framing of Women Figure in Indonesian Political
Stage
GRADEMARK REPORT
GENERAL COMMENTS
Instructor
PAGE 1
PAGE 2
PAGE 3
PAGE 4
PAGE 5
PAGE 6
PAGE 7
PAGE 8
PAGE 9
PAGE 10
PAGE 11
PAGE 12
PAGE 13
PAGE 14
PAGE 15
PAGE 16
PAGE 17
PAGE 18
PAGE 19
PAGE 20
PAGE 21
PAGE 22
PAGE 23
PAGE 24
PAGE 25
PAGE 26
PAGE 27
PAGE 28
PAGE 29


Check Similarity "The Framing of Women Figure in Indonesian Political Stage"

Bens, Pardamean

Form Reviewer "MOOC Model: Dimensions and Model Design to Develop Learning"

Girsang, Abba Suganda

Peer Review "Optimization of the Bugs Classification of the Ticketing System in Software Development: a Study Case"

Akun, Akun

Check Women Corruption and
Questions .pdf
by Arta Sundjaja
Submission date: 17-Oct-2019 10:53AM (UTC+0700)
Submission ID: 1102510027
File name: Women_Corruption_and_Questions.pdf (341.08K)
Word count: 3599
Character count: 18210




8%
SIMILARITY INDEX
8%
INTERNET SOURCES
0%
PUBLICATIONS
1%
STUDENT PAPERS
1 4%
2 3%
3 1%
4 1%
Exclude quotes On
Exclude bibliography On
Exclude matches < 1%
Check Women Corruption and Questions .pdf
ORIGINALITY REPORT
PRIMARY SOURCES
research.binus.ac.id
Internet Source
documents.mx
Internet Source
Submitted to Universitas Diponegoro
Student Paper
www.youtube.com
Internet Source


Similarity "Women, corruption, and questions of troubled women’s position in The troubled house: The unspoken ideology"

Meyliana, Meyliana

Check 33. Security and privacy
challenge in Bring.pdf
by Arta Sundjaja
Submission date: 12-Sep-2019 11:41AM (UTC+0700)
Submission ID: 1133697771
File name: 33._Security_and_privacy_challenge_in_Bring.pdf (306.87K)
Word count: 3783
Character count: 21176






30%
SIMILARITY INDEX
16%
INTERNET SOURCES
19%
PUBLICATIONS
16%
STUDENT PAPERS
1 6%
2 4%
3 3%
4 3%
5 2%
6 2%
7
Check 33. Security and privacy challenge in Bring.pdf
ORIGINALITY REPORT
PRIMARY SOURCES
research.binus.ac.id
Internet Source
Tanty Oktavia, Meyliana, Harjanto Prabowo,
Raymond Kosala, Suhono Harso Supangkat. "A
conceptual social learning ontology for higher
education in e-learning 2.0", 2016 International
Conference on Information Management and
Technology (ICIMTech), 2016
Publication
borneostudies.org
Internet Source
www.inderscienceonline.com
Internet Source
Submitted to Swinburne University of
Technology
Student Paper
www.tandfonline.com
Internet Source
Angelina Ervina Jeanette Egeten, Meyliana,
1%
8 1%
9 1%
10 1%
11 1%
12 1%
13 1%
Manik Hapsara, Harjanto Prabowo, Raymond
Kosala. "The general components of enterprise
architecture framework in e-Commerce: A
systematic literature review", 2017 International
Conference on Applied Computer and
Communication Technologies (ComCom), 2017
Publication
Sara Ali, Muhammad Nauman Qureshi, Abdul
Ghafoor Abbasi. "Analysis of BYOD security
frameworks", 2015 Conference on Information
Assurance and Cyber Security (CIACS), 2015
Publication
www.researchgate.net
Internet Source
Submitted to Universiti Teknikal Malaysia
Melaka
Student Paper
Submitted to Study Group Australia
Student Paper
Duy Dang-Pham, Siddhi Pittayachawan.
"Comparing intention to avoid malware across
contexts in a BYOD-enabled Australian
university: A Protection Motivation Theory
approach", Computers & Security, 2015
Publication
Taeeun Kim, Hwankuk Kim. "A system for
detection of abnormal behavior in BYOD based
14 1%
15 1%
16 1%
Exclude quotes On
Exclude bibliography On
Exclude matches < 1%
on web usage patterns", 2015 International
Conference on Information and Communication
Technology Convergence (ICTC), 2015
Publication
blog.hoosieremploymentlawyer.com
Internet Source
Submitted to Universiti Teknologi Malaysia
Student Paper
T. Andrew Yang, Radu Vlas, Alan Yang, Cristina
Vlas. "Risk Management in the Era of BYOD:
The Quintet of Technology Adoption, Controls,
Liabilities, User Perception, and User Behavior",
2013 International Conference on Social
Computing, 2013
Publication


Hasil Similarity "Security and privacy challenge in Bring Your Own Device environment: A Systematic Literature Review"

Binus University

Not a member yet