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Nurdiani, Nina

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John Freddy Bobby Saragih, Michael Tedja.
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CHOWANDA, ANDRY

 
LEMBAR  
HASIL PENILAIAN SEJAWAT SEBIDANG ATAU PEER REVIEW 
KARYA ILMIAH : PROSIDING* 
 
Judul Makalah : NYAM: An Android Based Application for Food Finding Using GPS 
Penulis Makalah : Andry Chowanda, S.Kom., M.M., Ph.D., MBCS., CCP 
Jumlah Penulis : 8 orang 
Identitas Pengusul : Penulis pertama/penulis ke-7/penulis korespondensi ** 
Identitas Prosiding :  a. Judul Prosiding : 3rd International Conference on Computer Science and 
Computational Intelligence, ICCSCI 2018 
  b. ISBN/ISSN : 1877-0509 
  c. Tahun Terbit, Tempat Pelaksanaan : 7-8 September 2018, Tangerang, Indonesia 
 d. Penerbit/organize : Elsevier B.V.  
 e. Alamat repository PT/web prosiding :  
 https://www.sciencedirect.com/science/article/pii/S1877050918314790/pdf?md5=2
bf6598c61594edfa245e6308e2bf1c5&pid=1-s2.0-S1877050918314790-main.pdf 
  f. Terindeks di (jika ada)   : Scopus 
 
Kategori Publikasi Makalah 
(beri √ pada kategori yang tepat) 
 Prosiding Forum Ilmiah Internasional Terindeks 
Prosiding Forum Ilmiah Internasional 
Prosiding Forum Nasional 
 
Hasil Penilaian Peer Review : 
  
 
Komponen Yang Dinilai 
Nilai Maksimal Prosiding  
Nilai Akhir 
Yang 
Diperoleh 
Internasional 
Terindeks 
Internasional Nasional 
a. Kelengkapan unsur isi paper (10%) 0.17   0.145 
b. Ruang lingkup dan kedalaman   
    pembahasan (30%) 
0.51   0.435 
c. Kecukupan dan kemutakhiran   
    data/informasi dan metodologi (30%) 
0.51   0.435 
d. Kelengkapan unsur dan kualitas terbitan/  
    prosiding (30%) 
0.51   0.435 
Total = (100%) 1.7   1.45 
Nilai Pengusul =  
 
Jakarta, 04-11-2020 
 
 
 
 
 
Dr. Derwin Suhartono, S.Kom., M.T.I. 
Unit kerja: Universitas Bina Nusantara 
 
*Dinilai oleh dua Reviewer secara terpisah 
**Coret yang tidak perlu 
 
 
 
 
 
 
 
 
  
 
√ 
 
 
√ 
 
 
 
LEMBAR  
HASIL PENILAIAN SEJAWAT SEBIDANG ATAU PEER REVIEW 
KARYA ILMIAH : PROSIDING* 
 
Judul Makalah : NYAM: An Android Based Application for Food Finding Using GPS 
Penulis Makalah : Andry Chowanda, S.Kom., M.M., Ph.D., MBCS., CCP 
Jumlah Penulis : 8 orang 
Identitas Pengusul : Penulis pertama/penulis ke-7/penulis korespondensi ** 
Identitas Prosiding :  a. Judul Prosiding : 3rd International Conference on Computer Science and 
Computational Intelligence, ICCSCI 2018 
  b. ISBN/ISSN : 1877-0509 
  c. Tahun Terbit, Tempat Pelaksanaan : 7-8 September 2018, Tangerang, Indonesia 
 d. Penerbit/organize : Elsevier B.V.  
 e. Alamat repository PT/web prosiding :  
 https://www.sciencedirect.com/science/article/pii/S1877050918314790/pdf?md5=2
bf6598c61594edfa245e6308e2bf1c5&pid=1-s2.0-S1877050918314790-main.pdf 
  f. Terindeks di (jika ada)   : Scopus 
 
Kategori Publikasi Makalah 
(beri √ pada kategori yang tepat) 
 Prosiding Forum Ilmiah Internasional Terindeks 
Prosiding Forum Ilmiah Internasional 
Prosiding Forum Nasional 
 
Hasil Penilaian Peer Review : 
  
 
Komponen Yang Dinilai 
Nilai Maksimal Prosiding  
Nilai Akhir 
Yang 
Diperoleh 
Internasional 
Terindeks 
Internasional Nasional 
a. Kelengkapan unsur isi paper (10%) 0.17   0.136 
b. Ruang lingkup dan kedalaman   
    pembahasan (30%) 
0.51   0.408 
c. Kecukupan dan kemutakhiran   
    data/informasi dan metodologi (30%) 
0.51   0.408 
d. Kelengkapan unsur dan kualitas terbitan/  
    prosiding (30%) 
0.51   0.408 
Total = (100%) 1.7   1.36 
Nilai Pengusul =  
 
Jakarta, 04-11-2020 
 
 
 
 
 
Dr. Fredy Purnomo, S.Kom., M.Kom. 
Unit kerja: Universitas Bina Nusantara 
 
*Dinilai oleh dua Reviewer secara terpisah 
**Coret yang tidak perlu 
 
 
 
 
 
 
 
 
  
 
√ 
 
 
√ 
 
 


Form Reviewer "NYAM: An Android Based Application for Food Finding Using GPS"

Tanuar, Evawaty

Heryadi, Yaya

Lukas, Lukas

Abbas, Bahtiar S.

Gaol, Ford L.

 
 
 
 
 
978-1-5386-9422-0/18/$31.00 ©2018 IEEE 
The 1st 2018 INAPR International Conference, 7 Sept 2018, Jakarta, Indonesia 85 
 
 
 
 
Using Machine Learning Techniques to Earlier 
Predict Student’s Performance  
 
Evawaty Tanuar  
1Computer Science Department, School 
of Computer Science 
2BINUS Graduate Program – Doctor of 
Computer Science 
Bina Nusantara University 
Jakarta, Indonesia 11480 
etanuar@binus.edu 
Yaya Heryadi 
Computer Science Department, BINUS 
Graduate Program – Doctor of 
Computer Science 
Bina Nusantara University 
Jakarta, Indonesia 11480 
yayaheryadi@binus.edu 
 
Lukas 
Cognitive Engineering Research Group 
(CERG) , Faculty of Engineering 
Universitas Katolik Indonesia        
Atma Jaya  
Jakarta, Indonesia 12930 
lukas@atmajaya.ac.id 
 
Bahtiar Saleh Abbas 
Computer Science Department, BINUS Graduate Program – 
Doctor of Computer Science 
Bina Nusantara University 
Jakarta, Indonesia 11480 
bahtiars@binus.edu 
Ford Lumban Gaol 
Computer Science Department, BINUS Graduate Program – 
Doctor of Computer Science 
Bina Nusantara University 
Jakarta, Indonesia 11480 
fgaol@binus.edu 
  
Abstract—Education field is rich of data, and machine 
learning used in this field are increasing lately. Based on their 
first semester result, using machine learning techniques, the 
student’s final year result (GPA) can be predicted. The data 
used in this experiment are from the computer science 
subjects, 6 subjects, 1 laboratories results and the GPA on 
their graduation year. The techniques used in this experiment 
are Generalized Linear Model, Deep Learning and Decision 
Tree. From this result, what are the important factors that 
impact on the result can be extracted to help the students 
prepared themselves earlier. 
Keywords—Machine Learning, Data Mining, Student’s 
Performance - GPA, Soft Computing 
I. INTRODUCTION 
Undergraduate Program, a bachelor’s degree, is one of 
the most important education as through this program, a 
person can increase his/her opportunities in the professional 
life. There are 5.6 million students that currently took 
undergraduate degree based on 2018 report of The ministries 
of Research, Technology and Higher Education[1]. Science 
is the third largest categories after education and social. 
The University that able to predict a student’s academic 
performance will be able to identify those students who can 
be awarded with scholarship and also able to identify the 
students that might be failed[2]. In both cases the more this 
information able to gather the more better for the students to 
catch up and prepare themselves. Currently a lot of data 
mining and machine learning technology are used in 
education field, in order to support both students and 
lecturers for better teaching and learning result. 
 
II. LITERATURE REVIEW 
Machine learning is part of the Artificial Intelligence 
(AI), where computer can teach themselves to learn the data. 
While data mining is a technique to find pattern in large 
amount of data[3]. 
 Machine learning used in education have much attention 
lately [4] For predicting student’s performance, the widely 
used model are instance-based learning, Naïve Bayes, 
Decision Tree, Artificial Neural Network, Support Vector 
Machine, Classification Tree [5], and clustering[6]. Different 
data can be used to analyzed the student’s performance, such 
as prelude of previous semester [7], pre-university and 
examination mark[8], and demographic attribute[9]. 
Generalized Linear Model (GLM) is the extend 
framework of linear regression[10]. The idea computing a 
hyperplane to minimize the loss function [11].  
Support vector machine is widely used for data mining 
and classification to predict membership of a data. It is based 
on the geometrical interpretation. The algorithm searches for 
optimal separating surface, such as hyperplane[12,13,14]. 
 Decision tree is one of the most common used 
techniques in predicting student’s performance[15]. A 
publication done in 2016, published about Predicting 
 
 
 
 
978-1-5386-9422-0/18/$31.00 ©2018 IEEE 
The 1st 2018 INAPR International Conference, 7 Sept 2018, Jakarta, Indonesia 86 
 
 
 
 
student’s final year GPA by the result of important courses 
that distributed in 8 semesters of studies[16]. 
Data mining in education field known as Educational 
Data Mining(EDM). It happen because of the increase of 
educational resources and data that can be explored to learn 
how a student learned [17]. Researchers also investigated the 
factors that influence learning outcomes[18,19,20]. 
 
III. DATA AND METHODOLOGY 
School of Computer Science is one of the largest 
department in Bina Nusantara University. There are 
approximately 6.000 active students each year. The data that 
used are the data from the graduates from 2017-2018, There 
are 1.835 data been used in this research. 
There data used are the result of 1st semester of 6 
subjects: Algorithm, English, Character Building, 
Programming Language Concepts, Calculus and Discreate 
Mathematic. Those subjects shows in the BINUS Computer 
Science department site[21]. The result of the Algorithm 
Laboratories also used in this research. For the targeted 
output, the final GPA (Grade Point Average) average are 
used.  
These data also divided into 2 different experiment. First 
experiment directly predicts based on the real GPA, while 
the second experiment, the GPA are categorized into 6 
groups. Below are the Distribution of GPA for the 
experiment on type 1. 
 
 
Fig. 1. Distribution of GPA for Data Type 1 
 
Fig. 2. Data correlation 
From the data correlation above, it shows that the GPA 
have high correlation to Final Algorithm, Discreate 
Mathematics and Calculus. While the Algorithm 
Laboratories result have a high correlation to Final 
Algorithm result.  
For type 2, the GPA are categorized into 6 groups 
according the rule GPA >=3.50, 3.50 – 3.00, 3.00 - 2.75, 
2.75 – 2.00, 2.00 - 1.80, and <1.80. The grouping is based on 
the criteria for graduation, High Distinction, Distinction, and 
to be able to graduate, students need to have at least 2.00 
GPA. This grouping based on the possibility to help those 
who still able to catch up or those not able to catch up.  
 
Fig. 3. Distribution of GPA Groups for Data Type 2 
 
 
 
 
 
 
 
978-1-5386-9422-0/18/$31.00 ©2018 IEEE 
The 1st 2018 INAPR International Conference, 7 Sept 2018, Jakarta, Indonesia 87 
 
 
 
 
Rapid miner [22] is used as the platform in this research. 
Three models are used in this research, General Linear 
Model, Deep Learning and Decision Tree. The preprocess 
data doing with collecting the final result from the 1st 
semester subjects and combine it with the final GPA. Using 
Rapid Miner, the model was developed and tested. The 
model are showed in Fig 4, 5, and 6. 
 
Fig. 4. General Linear Model 
 
Fig. 5. Deep Learning Model 
 
Fig. 6. Decision Tree Model 
IV. RESULT ANALYSIS 
The result of this experiment divided into two types, first 
type experiment on GPA and second type experiment on 
Group of GPA. 
A. Result on GPA Experiment 
The result on the first type data, where 1.835 data used to 
predict the GPA result.  
TABLE I.  ROOT SQUARE MEAN ERROR 
Model Root Mean Square Error Run Time 
Generalized Linear 
Model  
0.300 24 s 
Deep Learning 0.276 11 s 
Decision Tree 0.340 4 s 
 
From the table shows that Deep Learning method more 
accurate compare to others, while Decision Tree method was 
the fastest among the others model. These experiments, also 
shows that the important factors ranked from the result of 
algorithm subject, discreate mathematics and calculus 
subject.  
At the other hand the less important factors are Character 
Building, followed by English. In the decision tree, show in 
Fig 4, English, Character Building and Laboratory result of 
Algorithm did not count any. 
 
 
 
 
 
 
 
978-1-5386-9422-0/18/$31.00 ©2018 IEEE 
The 1st 2018 INAPR International Conference, 7 Sept 2018, Jakarta, Indonesia 88 
 
 
 
 
 
Fig. 7. Decision Tree Result 
From the decision tree result itself, the most contributed 
as a root is the final result of Algorithm, it need to be above 
66.5 so that can be group greater than D. The second most 
influenced factor is Discrete mathematics need to be above 
70.5 while the algorithm is more than 77.5 then it will be 
more likely to succeed in the undergraduate programs. 
The experiments also continue to reduce 2 data that less 
important as the result from the first GPA experiment, but 
the result did not improve. The Root Square Mean Error for 
Generalized Linear model became 0.309, Deep learning 
became 0.295 and it is same for the Decision Tree as it is not 
bringing any impact. 
B. Result on Group GPA Experiment 
The result on the first type data, where 1.835 data used to 
predict the GPA result.  
TABLE II.  ACCURACY RESULT 
Model Accuracy Run Time 
Generalized Linear  61.0% 2 s 
Deep Learning 59.9 % 4 s 
Decision Tree 51.5 % 3 s 
 
This type of data easier for the data mining to do the 
learning process compare to the previous experiment. 
The less important factors, English and Character 
Building also removed for experiment, but show none 
improvement, for Generalized Linear model became 59.9%, 
Deep learning became 59,1% while there is small 
improvement for Decision Tree, became 52%. 
The experiment also shows that the most important 
factors are: Algorithm final result, Discreate mathematics 
and Calculus subjects. 
C. Result on Group GPA Experiment Advanced 
In addition to ensure the result on GPA experiment, 
another 2 programming subjects added to advance 
experience which are Data Structure and Object-Oriented 
Programming. The result shows in below table. 
TABLE III.  ACCURACY RESULT 
Model Accuracy Run Time 
Generalized Linear  66.6% 1 s 
Deep Learning 67.6 % 5 s 
Decision Tree 60.6 % 2 s 
 
By adding more subjects, the importance factors are 
changed. The new important factors are: Algorithm Final 
Result, Data Structure, Object oriented, and discreate 
mathematics. 
D. Simulator for predictiong result. 
There also a simulator for student’s future used to predict 
the possibility group of result or the GPA. Hopefully by 
using this simulator, students can prepare themselves better. 
Student only need to insert their current results to predict 
their probability in the final year GPA. 
V. CONCLUSION 
From these data mining experiments using rapid miner on 
1st semester result to predict the Final Year GPA, showed 
that the grouping of GPA is more better in term of run time 
compare to the other type of data. By grouping the result is 
better for prediction itself and also for the students to know 
their ability. Decision tree is the easiest model that can be 
accurately introduced to users. 
The experiment also shows that the most important 
factors are: Algorithm final result, Discreate mathematics 
and Calculus subjects. Even in the additional Experiment on 
another 2 subjects for 2nd semester, the programming skills 
are the important factor for computer science students. So, 
for student that want to get a good result need to get a higher 
mark or better understanding those subjects as also it is true 
they are the basic knowledge needed for computer science. 
Different type of data and information besides the result, 
such as previous examination mark or others psychology test 
also can be added into the data for future research.  Also, for 
future research, progressive prediction also can be done by 
adding the progress result of the second semesters. 
 
 
 
 
 
978-1-5386-9422-0/18/$31.00 ©2018 IEEE 
The 1st 2018 INAPR International Conference, 7 Sept 2018, Jakarta, Indonesia 89 
 
 
 
 
In conclusion that the data mining from previous 
student’s experience are able to bring knowledge for future 
students. By having and understand the information, the next 
students will be more prepared than before.  
 
Fig. 8. Simulator for prediction 
ACKNOWLEDGMENT 
Thanks to School of Computer Science, Bina Nusantara 
University, for the data in this research, so this research able 
to be done and hope can bring benefit for all the Computer 
Science students. 
REFERENCES 
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[21] U. Binus, “Course Structure,” 2015. [Online]. Available: 
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English

Using Machine Learning Techniques to Earlier Predict Student’s Performance

Maharani, Anita

Reviewer "New Media And Recruitment"

Check 25. Measurement
effectiveness and efficiency.pdf
by Arta Sundjaja
Submission date: 17-May-2019 02:54PM (UTC+0700)
Submission ID: 1102511214
File name: 25._Measurement_ef f ectiveness_and_ef f iciency.pdf  (573.12K)
Word count: 3656
Character count: 20182






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Check 25. Measurement effectiveness and efficiency.pdf
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aut.researchgateway.ac.nz
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www.scirp.org
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www.ukessays.com
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f ile.scirp.org
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www.lap-publishing.com
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Eric Gunawan, Indrajani Sutedja. "Using
Enterprise Architecture with the Open Group
Architecture Forum to Design Information
Technology Plan Gap Analysis at Bank
Pengkreditan Rakyat (BPR)", 2018 International
Conference on Information Management and
Technology (ICIMTech), 2018
Publicat ion
Maniah, Meyliana, Achmad Nizar Hidayanto,
Harjanto Prabowo, Ford Lumban Gaol. "The
Trigger Factors and Constraints on e-Supply
Chain Processes: A Systematic Literature
Review", 2018 International Conference on
Information Management and Technology
(ICIMTech), 2018
Publicat ion
Submitted to Laureate Higher Education Group
Student  Paper
scholar.binus.ac.id
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Check Similarity "Measurement effectiveness and efficiency to improve the IT services using ITSM"

Check 38. Software size
measurement with use case
point.pdf
by Arta Sundjaja
Submission date: 17-May-2019 03:09PM (UTC+0700)
Submission ID: 1102519417
File name: 38._Sof tware_size_measurement_with_use_case_point.pdf  (318.3K)
Word count: 3216
Character count: 16230





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Check 38. Software size measurement with use case point.pdf
ORIGINALITY REPORT
PRIMARY SOURCES
Safrizal, Harco Leslie Hendric Spits Warnars,
Ford Lumban Gaol, Edi Abdurachman. "Use
case point as software size measurement with
study case of Academic Information System",
2017 IEEE International Conference on
Cybernetics and Computational Intelligence
(CyberneticsCom), 2017
Publicat ion
Dede Kurniadi, Sasmoko, Harco Leslie Hendric
Spits Warnars, Ford Lumban Gaol. "Software
size measurement of student information
terminal with use case point", 2017 IEEE
International Conference on Cybernetics and
Computational Intelligence (CyberneticsCom),
2017
Publicat ion
Rhio Sutoyo, Harco Leslie Hendric Spits
Warnars, Ford Lumban Gaol, Edi
Abdurachman, Benfano Soewito.
"Measurement of QuestDone mobile
application using 7 steps use case points
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method", 2017 IEEE International Conference
on Cybernetics and Computational Intelligence
(CyberneticsCom), 2017
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Ch. Ram Mohan Reddy, D Evangelin Gcetha, T
V Suresh Kumar, R V Raghavendra Rao.
"Framework for Integrating SPE with Web
Services Development Model", 2018 3rd
International Conference for Convergence in
Technology (I2CT), 2018
Publicat ion
www.bfpug.com.br
Internet  Source
people.eecs.ku.edu
Internet  Source
Bente Anda. "Improving Estimation Practices by
Applying Use Case Models", Lecture Notes in
Computer Science, 2002
Publicat ion
D Kurniadi, A Mulyani, Y Septiana, H Aulawi.
"Estimated software measurement base on use
case for online admission system", IOP
Conference Series: Materials Science and
Engineering, 2018
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ww1.ucmss.com
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Evawaty Tanuar, Harco Leslie Hendric Spits
Warnars, Ford Lumban Gaol, Edi
Abdurachman, Benfano Soewito. "Measuring
Anticipated User Experiences for Enhancing
Development, Case Study on E-commerce",
2018 International Conference on Information
Technology Systems and Innovation (ICITSI),
2018
Publicat ion


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