Decision Support System for Determination of Scholarship Using Scratch Programming Based on SAW Method

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Decision Support System for Determination of Scholarship Using Scratch
Programming Based on SAW Method
Article · December 2021
DOI: 10.47927/ijobit.v2i2
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DECISION SUPPORT SYSTEM FOR DETERMINATION OF
SCHOLARSHIP USING SCRATCH PROGRAMMING
BASED ON SAW METHOD
Dentaruni Cahya Purnomo1, Indra Gunawan2
1,2Sekolah Tinggi Teknologi Ronggolawe, Indonesia
igunsttr@gmail.com
Article Information ABSTRACT
Received: Oktober 2, 2021
Revised: November 6, 2021
Online: Desember 25, 2021
• Scholarships are one of the efforts to provide financial
assistance or support to individuals who are expected to be
able to support the need in the process of taking education. In
2020, 1st State Vocational School of Cepu had more than 1000
students and from 300 scholarship submissions only 9 people
managed to get a scholarship. Therefore a tool is needed to
select students who are entitled and less entitled to get
scholarships. Decision Support System is one of the tools that
can be used to calculate to select the students. The method
used is Simple Additive Weighting(SAW) because the method
is a simple but it’s still an accurate method, because it has a
very low error level. The aim of this study is to apply the Simple
Additive Weighting(SAW) method for Decision Support
System scholarships using Scratch Programming which is a
programming language that is fairly newly developed and
easy to apply to various attractive creations with a shorter
time compared to using other programming languages. The
criteria used in this study are parent’s income, parent’s
dependents, Home-to-school Distance , and the students age.
The results obtained and analyzed from this study concluded
that the testing system using Mean Square Error(MSE) has a
very low level of error which is equal to 1.3312E-31 it can be
said that the system is accurate, and from the test
functionality using the Black Box obtained 100% results.
•
Keywords
Decision Support System, Simple
Additive Weighting, Scratch,
Scholarship Selection
INTRODUCTION
Education is one of the important factors that can support the life of a nation. The provision that
every citizen has the right to study has been mentioned in the 1945 Constitution. Therefore, the
government agreed to build a 12-year compulsory education program (Nugroho, 2014). Scholarships
are one of the efforts to provide financial assistance or support to individuals who are expected to be
able to support the need in the education process (Yulianti, 2013).
Based on information obtained through interviews by the Administration of 1st State Vocational
School of Cepu, scholarships given to students are divided into three types, namely poor students'
scholarships (BSM), smart Indonesian students (PIP), and Zakat (UPZ) (UPZ) units. Of the three
scholarships the author will conduct a selection process to determine the feasibility of recipients of poor
student scholarships that have limited number of recipient quota. The criteria that are references are
parents' income, the number of parents, the distance of student houses, and the age of the student. In
2020 1st State Vocational School of Cepu had more than 1000 students and from 300 scholarship

International Journal of Business and Information Technology Vol. 2 No. 2, Desember 2021
IJOBIT Page 73
submissions that managed to get the help of only 9 children. From these data it can be concluded that
quite a number of students must be assessed to know which students are worthy of the scholarship.
The problem that arises is the process of selecting scholarship recipients having a shortage or
still not on target because it still uses a manual system, namely by means of being thoughtful and still
susceptible to human factors. Therefore a decision support system(DSS) is needed to assist the process
of resolving problems and as a tool for supporting decision-making processes with established criteria
(Kusrini, 2007).
One method in the decision support system is the SAW (Simple Additive Weighting) method.
The SAW method is often used to assist in decision making because this method has advantages, namely
the assessment process will be more appropriate because it is based on the criteria value of weight, and
predetermined preferences, as well as the calculation of the normalization of the matrix that is adjusted
to the value of the attribute (consecutive benefit or worth costs). It can be concluded that this SAW
method determines the weight value of each criteria for determining optimal alternative (Chung, 2018).
Compared to other DSS methods such as Technique for Order Preference by Similarity To Ideal
Solution (Topsis) by (Suyanti, 2018) resulting in the conclusion that the implementation of the SAW
method turned out to be better than the implementation of the Topsis method, this was seen in the
results of the sensitivity test, SAW by 1, 41% and the tops of 0.69% (Suyanti, 2018). Comparison analysis
of SAW, Topsis and WP use Hamming Distance concluded that the method is closest to the results of the
decision, namely the Saw and Topsis method, so that the method is feasible to use the decision-making
process objectively (Kungkung, 2018).
The criteria used in research by (Topadang,), (Bhakti) and (Wolo et al, 2018) for providing poor
scholarships just use 3 criteria and criteria most often arise are parents' income and parents, however
The distance of the student's house will affect student spending and age of students also affect the
priority of students in the process of selecting to get a scholarship. So the author will use the criteria for
parent’s income, parent’s dependants, Home-to-school Distance, and students age in this study.
LITERATURE REVIEW
Here are some previous studies about the decision support system (DSS) using the Simple
Additive Weighting (SAW) method that had been carried out by several previous writers. Comparisons
of these studies can be seen in Table 2.1, with the results of the following research:
The first study was conducted by (Teja et al, 2020) entitled "The Simple Additive Weighting
Method in Determining the Recipient of the John Paul's School Junior High School Achievement
Scholarship" The results of the study are the Method of Simple Additive Weighting (SAW) can effectively
determine the recipients of the achievement scholarship in Junior John Paul's School with a presentation
of value above 80% using three criteria, namely the value of English test, the math test value, and
interview test value. But in this study it has not been applied to an application.
The second research was conducted by (Taufik et al, 2020) entitled "Web-based scholarship
decision support system using the Simple Additive Weighting (SAW) method in the Daarul Ahsan Islamic
Boarding School". The results of the study are the Simple Additive Weighting (SAW) method can help
the problem and help effectively in the Daarul Ahsan Islamic Boarding School to determine scholarship
recipients. In this study, a web application was created by PHP programming language.
The third research by (Sadewa et al, 2021) entitled "System Support Decision The feasibility of
scholarships using the Simple Additive Weighting method" The final result that can be obtained from
this study is the Simple Additive Weighting (SAW) method at PT. Novell Pharmaceutical Laboratories
can help produce decisions quickly and effectively regarding the feasibility of scholarship recipients.
This research has been created an application using Java programming.
METHODOLOGY
The research methodology described through a diagrams is needed to plan a process, analysis,
and documentation, as it is a guideline for conducting research. The Input Process Output(IPO)
approach is used to describing the structure of an information processing or other process that exist in
the system. The IPO method used can be seen in Fig 1.

IJOBIT Page 74
INPUT PROCESS OUTPUT
Scholarship Criteria
1.Parent Earnings (C1)/Cost
2.Parent’s dependents (C2)/Benefit
3.House distance (C3)/Benefit
4.Age of students (C4)/Cost
DECISION SUPPORT SYSTEM
Based on SAW
Using Scratch Programming
Feasibility Recommendations
to Receive Scholarships
Figure 1. Research Methodology
Input is a step of preparing, collecting and entering data from the outside into the system. The
variable used in the Input are: 1. Parent Earning, we encode it to C1. it is a Cost variable, it means the
smaller value will have higher weight score and vice-versa. 2. Parent’s dependant(C2) as a Benefit
variable. 3. Home-to-school Distance(C3) as a Benefit variable. 4. Age of Student(C5) as Cost variable.
Process is a step that we do a design and implementation using Scratch programming language into an
application. Afterwards, all of data will be inputed, calculated and processed to produce an output using
SAW algorithm. After the step is complete, the application will produce a score that also explains the
feasibility to receive a scholarship of user.
After the Input, Process and Output are declared through an IPO diagram. We then develop the
application using Waterfall methodology that consist of 5 steps: 1. Requirement Definition. 2. System
and Software Design. 3. Implementation. 4. Integration. 5. Finding and Conclusion. We describe the
waterfall methodology that we used using flow diagram that can be seen in Fig 2.
Start
Requirement
Definition
System and
Software Design
Implementation
Integration
Finding and
Conclusion
End
Figure 2. Research Flow Diagram
The Design stage is conducted using an UML diagram namely: Use Case Diagram, Activity
Diagram, and Sequence Diagram. Those diagrams are used as a blueprint. Then, the blueprint is
converted to an application at the development stage using a tool namely Scratch that can be
downloaded from http://scratch.mit.edu.
The algorithm used in the application is a SAW. It is the best and simpliest method for evaluating
a number of alternatives in terms of a number of decision criteria. The Simple Additive Weighting (SAW)

IJOBIT Page 75
method is a method based on solving problems in making decisions by determining the most optimal
alternative of a number of alternatives using a predetermined crateria.
The basic concept of the Simple Additive Weighting (SAW) method is seeking a bureably from a
performance rating on every alternative of all the criteria used to take decisions. Therefore the Simple
Additive Weighting (SAW) method is often known as a bureauted method. The calculation using the
Simple Additive Weighting (SAW) method will produce the value of the preference and the greatest
value that will be used as the best alternative. In the Simple Additive Weighting (SAW) method
determines the weight on each criterion will greatly affect the final results or preference value.
Here are the steps to complete the Simple Additive Weighting (SAW) method:
1) Determine the alternative symbolized by AI.
2) Determine the various criteria that will be used in making decisions, symbolized by CI.
3) Specifies the compatibility rating of alternatives for all criteria.
4) Determine the weight of interest or the degree of interest for each criteria.
5) Creating tables containing a matching rating for any alternative to all criteria.
6) Changing the compatible rating table that has been determined into a decision matrix, where i = 1.2
... m and j = 1.2 ... n
𝜒
= [
𝑟11 𝑟12 …
⋮
𝑟𝑖1 𝑟𝑖2 …
𝑟𝑖𝑗
⋮
𝑟𝑖𝑗
]
7) Calculate the normalist matrix of the decision matrix by determining the type of attribute (benefit
or cost) for each CI in each AI. The equation used to calculate the normalization matrix is as follows:
𝑟𝑖𝑗
=
{
𝑥𝑖𝑗
𝑀𝑎𝑥𝑖 𝑥𝑖𝑗
𝑀𝑖𝑛𝑖 𝑥𝑖𝑗
𝑥𝑖𝑗
if j is a 𝑏𝑒𝑛𝑒𝑓𝑖𝑡 variable
if j is a 𝑐𝑜𝑠𝑡 variable
Description :
• RUH = the expreaded performance rating
• Maxi = maximum value of the line criteria to i
• Mini = minimum value of line criteria to i
• Xij = row value and column from the matrix
The results of the predictable performance rating calculation will form the tinged matrix, symbolized by
R.
𝑅 = [
𝑟11 𝑟12 …
𝑟𝑖1 𝑟𝑖2 …
𝑟𝑖𝑗
𝑟𝑖𝑗
]
Calculating the preference (VI) value that will be obtained from the predetermicized matrix
multiplication (R) with a degree of interest or weighing (W) that has been determined for each criterion
(CI) of an alternative (AI) which is then summed up the whole. Here are the equations used to calculate
preference values:
𝑉𝑖 = ∑ 𝑊
𝑗 𝑟𝑖𝑗
𝑛
𝑗=1

IJOBIT Page 76
Description :
• Vi = final value of alternative or preference
• WJ = Weight or Degree of Affected Interest
• RIJ = normalization matrix element
RESULT
Data
The criteria for selecting scholarship grantee candidate was gathered from the literature and
combined to the data from the object of the research as it can be seen in the Table 1.
Table 1. Data Variable
No Criteria Name (𝑪𝒊) Category Weight (%)
1 Parent’s Earning (C1) Cost (min) 30
2 Parent’s Dependant (C2) Benefit (max) 30
3 Home-to-school Distance (C3) Benefit (max) 20
4 Student Age (C4) Cost (min) 20
After the scholarship grantee candidate data from the object of the research been gathered, we
classified the Parent Earning into 5 categories as can be seen in Table 2.
Table 2. C1 (Parent Earning)
C1 Weight
Rp.0-500.00 1
>Rp.500.000-1.000.000 2
>Rp.1.000.000-2.000.000 3
>Rp.2.000.000-3.000.000 4
>Rp.3.000.000-4.000.000 5
We make a maximum Parent’s Dependant categories to 4, given that the number above 4 is a rare case.
Hence, we classified it into 5 categories as can be seen in Table 3.
Table 3. C1 (Parent Earning) and C2 (Parent’s Dependant)
C2 Weight
0 1
1 2
2 3
3 4
>4 5
The latest regulations that have been issued by the Ministry of Education said that every
graduate can only register at the nearest school. The distribution of graduates and school zoning is
regulated in detail by the authorized party. Therefore we only create a maximum distance category to
20 killometers based on that regulation. The five categories of Home-to-school Distance can be seen in
Table 4.
Table 4. C3 (Home-to-school Distance)
C3 Weight
0-5 Km 1
>5-10 Km 2
>10-15 Km 3
>15-20 Km 4
>20 Km 5
In Indonesia in general the age of students on average is only one to two years of difference. This
happens because, at the primary education level applies the minimum age rule is 6. So that the age of

IJOBIT Page 77
students at the high school level is also relatively the same. Then, we classified age of student data into
5 categories from 14 to >17. It can be seen in Table 5.
Table 5. C4 (Age of Student)
C4 Weight
14 Years 1
15 Years 2
16 Years 3
17 Years 4
>17 Years 5
Designing the application through a diagrams is an initial picture to build a system. The
application will be made based on use case diagram, sequence diagram, activity diagram, and user
interface design. The Use Case Diagram is a functional picture of a system by describing actors and its
relationship with the system. Use Case Diagram describe how the actor interact with other actor and the
system. It can be seen in Fig 3.
Figure 3. Use Case Diagram
Sequence diagrams describe the activity of the object by describing the life time of objects and
messages sent and received between objects. The number of sequence diagrams made at least have to
define all parts of the use case that has been made before. Here we only display sequence diagrams in
the main process, namely the calculating process. The diagram can be seen in the Fig. 4.
Figure 4. Sequence Diagram of Calculation Process

IJOBIT Page 78
Activity diagrams are design activities or work flow in the system to be run. Activity diagram is
used to define or group the flow of display from the system. The following is the Activity Diagram that
describe the work flow of the system that will be built as it can be seen in Fig.5
Figure 5. Activity Diagram
After the entire design stage is completed, the application development is carried out using
Scratch Programming. The Decision Support System of Scholarship Selection Grantee Candidate based
on SAW algorithm can be seen in Fig. 6
Figure 6. Scholarship DSS App Powered by Scratch Programming
CONCLUSION
Based on the results of the research system of decision supporting scholarships using Scratch
Visual Programming which has been described in the previous chapter can be withdrawn several
conclusions, namely: 1. The decision support application that has been made using Scratch Visual

IJOBIT Page 79
Programming with the Saw method developed can run according to the design that has been created
and based on the test results using the MSE system developed has a very low error level of 1.3312E-31,
it can be said that the system is accurate. 2. The results of testing functionality using the Black Box
obtained by 100%. According to the testing carried out the application can run well.
In this study there are still a number of shortcomings that must be corrected in future research.
Here are some suggestions that can be used as a reference for future research: 1. On the system
developed does not have a database on the cloud server. The storage is only on a temporary memory. 2.
It is expected to add other users such as the principal or other and the addition of data editing features
or applying with algorithms other than SAW. 3. It is expected to be able to change the way the input of
student data and the criteria used from the manual using the keyboard becomes automatic just by
clicking.
REFERENCES
Bhakti, S. P. R. (2017). Seleksi Beasiswa Siswa Miskin Bsm Di Smk Islam Sudirman Andong Dengan
Metode Simple Additive Weigthing (Saw) (Doctoral Dissertation, Stmik Sinar Nusantara
Surakarta).
Chung, P. S. (2018). Studi Kasus Sistem Penunjang Keputusan Metode SAW dan TOPSIS. Malang: CV.
Seribu Bintang.
Kungkung, A., & Kiswanto, R. H. (2018). Analisa Perbandingan Metode SAW, WP dan TOPSIS
Menggunakan Hamming Distance. Konferensi Nasional Sistem Informasi (KNSI) 2018.
Kusrini, K. (2007). Aplikasi Sistem Pendukung Keputusan. Yogyakarta: Andi.
Nugroho, S. B. M. (2016). Pengaruh Pendidikan Terhadap Pertumbuhan Ekonomi. Media Ekonomi dan
Manajemen, 29(2).
Sadewo, B., Akhirina, T. Y., & Suhaemi, T. (2021, January). Sistem Pendukung Keputusan Kelayakan
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Informasi, 3(3), 1208-1225.
Taufik et al, R, ”Sistem Pendukung Keputusan Penerima Beasiswa Berbasis WEB Menggunakan Metode
Simple Additive Weighting ( SAW ) pada Pondok Pesantren Daarul Ahsan”, Jurnal Informatika
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Pondok Pesantren Daarul Ahsan. Jurnal Informatika Universitas Pamulang, 5(1), 54-60.
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