APPLICATION OF THE SIMPLE ADDITIVE WEIGHTING METHOD IN DECISION SUPPORT SYSTEMS AT ISLAMIC BOARDING SCHOOL OF SYAICHONA MOH. CHOLIL GAMBUT

(1)

Al Ulum: Jurnal Sains dan Teknologi (e-ISSN 2477-4731) Vol. 9, No. 2, 2023 DOI: http://dx.doi.org/10.31602/jst.v9i2.11677

Uniska PPJ-JST

APPLICATION OF THE SIMPLE ADDITIVE WEIGHTING METHOD IN DECISION SUPPORT SYSTEMS AT ISLAMIC BOARDING SCHOOL OF SYAICHONA MOH. CHOLIL GAMBUT

Muhammad Fajrian Noor • Syarifil Anwar • Sofyar • Marjuatul Khairiah

Received: 19 June 2023 | Accepted: 07 July 2023 | Published online: 10 August 2023 UPT Publication and Journal Management Uniska-JST 2023

Abstrac

t At Syaichona Moh. Cholil Islamic Boarding School Gambut is one of the events where students will be selected and have the right to be made outstanding students, usually called student stars. To simplify, it is necessary to create a decision support system (DSS). In the SPK selection of outstanding students, the Simple Additive Weighting (SAW) method was used.

This method can determine the weight value for each criterion, followed by a ranking process to select the best alternative from all the alternatives. The student achievement data used is data from the test score of class 4 students at the MI level graduating in 2020. In the DSS test using the SAW method, it can be concluded that the highest score with a value of 0.90 is the value recommended by the user to determine outstanding students.

Keywords:

Selection of Outstanding Santri ∙ SPK ∙ SAW ∙ DSS

 Sofyar

[email protected]

Program Studi Teknik Informatika, Universitas NU Kalimantan Selatan, Indonesia

Introduction

Being an outstanding student is an honor. Not only proud of his family but also proud of himself and the school. School appreciation for students who excel is important so that students who get achievements can be even more motivated to achieve even higher achievements, for other students it can also be motivation so they can achieve these achievements.

Syaichona Islamic Boarding School Moh.

Cholil Gambut was raised by Ustadz Ahmadi and was inaugurated in 1995 which is located at Jl.

Pemajatan, West Peat, Gambut District, Banjar Regency, South Kalimantan. This Islamic boarding school is the 4th branch of the Syaichona Moh. Cholil Islamic Boarding School that exist throughout Indonesia. Among the students at the Syaichona Moh. Cholil Islamic Boarding School Gambut has one of the events where students will be selected and have the right to be made as outstanding students. The event is usually called student stars where the selected students will be awarded twice a year. In determining the selection of outstanding students at the Syaichona Moh. Cholil Islamic Boarding School Gambut still uses the manual method but not automatic. To facilitate the selection of outstanding students, we need a decision support system that makes the process of analysis and decision making easier and faster.

The method of choice for selecting outstanding students is Simple Additive Weighting (SAW). Simple additive weighting method is a method that is widely used in making decisions that have a lot of attributes, so that by ORIGINAL ARTICLE

(2)

Al Ulum: Jurnal Sains dan Teknologi (e-ISSN 2477-4731) Vol. 9, No. 2, 2023 68

Uniska PPJ-JST applying the method of SAW on decision support

systems the completion of various decision- making processes can be easily (Nurmalini, 2017). The SAW method was chosen because it was able to select the best alternative from several alternatives (Painem, 2019). The basic concept of the SAW method is to find the weighted sum of the performance evaluations for each choice of all attributes. The SAW method requires a process to normalize the decision matrix (X) to a scale that can be compared with all existing alternative scores (Limbong, 2002). The rating system is based on predetermined criteria and weights and is expected to provide a more accurate assessment.

Based on this, the idea emerged to apply the SAW method to a decision support system for selecting outstanding students. This SAW (Simple Additive Weighting) method can enable decision making with more efficient results. The introduction of this decision support system is expected to reduce subjectivity in selecting outstanding students at the Syaichona Moh.

Cholil Islamic Boarding School Gambut.

The decision support system is known as Decision Support Support (DSS). It is a computerized information system that is used to support decision making within a company or organization (Evitasari, 2021).

Figure 1. SPK components

The figure 1 shown components of the decision support system consist of:

1. Database Management (Data Management) is a data subsystem that is organized into a database

2. Base models (management models) are models that are widely used in decision

making processes and can be divided into two types, namely:

a. Information Models, creating an information model in a Decision Support System using a Web-based programming language with PHP and MySQL.

b. Mathematical Models, decision Support Systems use algorithms where decisions are made by developing and comparing alternative ratings.

3. User Interface (User Interface) is a unification between two components, namely data management and management models which are combined into a third component (User Interface), presented in the form of a model that can be understood by computers (Adani, 2021).

According to Limbong (2002) the basic concept of the Simple Additive Weighting (SAW) method is to find the weight of the overall performance score for each attribute selection. The SAW simple weighting method requires the process of normalizing the X decision matrix to a scale comparable to all available alternative estimates.

An achievement Santri is a student studying religion at a boarding school who has the best grades in both academic and non- academic fields. The first study by Mulyati (2016) entitled application of the simple additive weighting method for determining marketing priority for beef meatball product packaging, concluded that this system can help speed up marketing management performance in obstacles to conducting assessments to select packaging to be prioritized in agent distribution and with the Method Simple Additive Weighting in this application, the process of calculating each weight and criteria is faster in selecting packages to be prioritized in agent distribution.

The second study by Permatasari (2016) entitled decision support system for determining majors at kader bangsa islamic vocational schools using the SAW method, concluded that the results of system calculations were ranking the highest value to the lowest and the highest

(3)

Uniska PPJ-JST value was the result needed as consideration by

the user to determine selection of majors in SMK.

In this study, it has a focus on the problem of decision support systems for selecting outstanding students at the Syaichona Moh.

Cholil Peat Islamic Boarding School by applying the simple additive weighting (SAW) method which aims to produce a decision support system that is able to provide fast and accurate decision results so as to facilitate users in the process of selecting outstanding students.

Materials and Methods

Data Analysis

Data analysis in this study used SAW method.

The system requirements analysis carried out by the administrator uses a use case diagram, namely as shown in Figure 2 and Table 1 below:

Login

Tambah Alternatif

Lihat Alternatif

Ubah Alternatif

Hapus Alternatif

Tambah Nilai

Lihat Nilai

Ubah Nilai

Hapus Nilai

Proses Perangkingan

Report Perangkingan

Logout

Figure 2. Admin Use Cases

Table 1. Use Case Summary No Use case

Name Description

1 Login Admin initial process to enter the admin page

2 Add

Alternative

Add alternative data to the database

3 View

Alternatives

View all alternative data that has been entered

4 Change

Alternative

Changing alternative data in the database 5 Delete

Alternative

Delete alternative data in the database 6 Add Value Add value data to the

database 7 View Value View all value data that

has been entered 8 Change Value Changing the existing

value data in the database 9 Delete Value Delete existing value data

in the database

10 Ranking Process

The process of calculating the weight value system

for each criterion and seeing the final ranking

results that have been processed 11 Ranking

Report

Report on the results of ranking which can be

saved in pdf format 12 Logout

The admin process to exit the admin page and will

return to the login page System Planning

Logical Design

In system planning, the logic design is shown in Figure 3.

Figure 3. Logical Design

Data Management Design

On the data management design (figure 4) for logins is shown in table 2, while alternatives,

(4)

Uniska PPJ-JST alternative registration and ranking are shown in

tables 3, 4, and 5, respectively.

Table 2. Logins

Column Name Type Description Username Varchar(10) Column to store

Username Password Varchar(255) Column to store

Password

Level Varchar(50)

Table 3. Alternatives Column

Name Type Description

Registration

number Int(11)

Column to store the student identification number

Name santri Varchar (50)

Column to store the name of the student Level Varchar

(25)

Column to store education level Class Varchar

(10) Column to store classes

Table 4. Alternative registration Column

Name

Typ

e Description Id_register Int(

11)

Column for storing outstanding student registration id

Reg_date Dat

e

Column to save registration date year

Var char

(4)

Column to store the academic year of students

Registration_

number

Int(

11)

Column to store the student identification number

Average_

value

Int(

3)

Column to store the average value criteria Moral_values Int(

3)

Column for storing moral value criteria

Memorization _ value

Int(

3)

Column for storing memorization value criteria

value_alquran Int(

3)

Column for storing the criteria for the value of the Koran

Table 5. Ranking Column

Name Type Description Rank_id Int(11)

The ranking number column is the result of the ranking process

Register_

id Int(11)

Column for the identity of the students who have registered or the student identification number Average

_n

Decima l(3,2)

Column matrix

normalization results for the average value moral_n Decima

l(3,2)

Column normalized matrix results for moral values Memoriz

ation _n

Decima l(3,2)

Column normalized matrix results for memorized values

Al- Qur'an_n

Decima l(3,2)

Column normalized matrix results for the value of the Koran

preferenc e

Decima l(3,2)

The final result column is the value of the matrix ranking process

Figure 4. Model management design

(5)

Uniska PPJ-JST Dialog Management Design

Admin page

On this page an admin can enter student data, student scores according to existing criteria, and carry out the ranking process. Figure 5 displays the dialog management design on the admin page, consisting of login, home, students, registration, ranking, and ranking report cards.

Figure 5. Dialog management design

Results and Discussion

System implementation requirements

System implementation requirements, namely software and hardware specifications used to support the implementation of this decision support system are shown in table 6.

Table 6. System implementation requirements table

Software Hardware

The operating system used is microsoft windows 10, 64-bit

System manufacturer acer model aspire a315-42

The programming

language used is ahp with the apache XAMPP web server application, sublime text 3 text editor

The processor used is AMD ryzen 3 3200U with tadeon Vega mobile gfx 2.60 GHz

The database used is Mysql

The memory used is 4 GB

Data Management Implementation

The data management used to implement data in the decision support system using the Simple Additive Weighting (SAW) method is to create

a database with the name

"tes_db_santri_prestasi" (MacCrimon, 1968).

Model Management Implementation

Implement settlement steps using the SAW method with manual calculations as shown in Figure 4 and enter alternative data (table 7).

Table 7. Alternative Value Data

Name Altern

ative Aver

age Valu e

Mor al Valu

e Me mori

zed Valu e

Al- qur’

an Valu e Muhammad Sidiq A1 0,93 0,55 0,6 0,8

Dion Satriwan A2 0.89 0,65 1 0,75

Aswari Ilham A3 0,84 0,56 0,9 0,45

Selamet A4 0,85 1 0,75 0,8

Ahmad Ramadahani

A5 0,93 0,75 1 0,85

Muhammad Riski A6 0,91 0,8 0,62 0,88

M. Haris A7 0,87 0,88 1 0,75

Syaipul Anwar A8 0,94 1 0,95 0,77

M. Syaifudin A9 1,00 1 0,85 0,9

Fahrul Evendi A10 0,89 0,65 0,8 0,85

Moh. Husen A11 0,91 0,65 1 0,68

M. Ridho Rizki Anshari

A12 0,92 0,75 1 0,78

Asyroful Anam A13 0,98 1 0,85 1

Rizkiy Ardian A14 0,87 0,75 0,61 0,7

Define Criteria (table 8).

Table 8. Criteria

Criteria (C) Description

C1 Average value

C2 Morals

C3 Memorized

C4 Al-qur’an

(6)

Uniska PPJ-JST Determine the weight value of each criterion

(table 9).

Table 9. Criteria Weight

Criteria C

Description weight value

C1 Average value 60%

C2 Morals 20%

C3 Memorized 10%

C4 Al-qur’an 10%

Conformity rating value of each alternative on each criterion (table 10).

Table 10. Compatibility of alternatives for each criterion

Alternatif

Criteria

C1 C2 C3 C4

A1 81 55 60 80

A2 77 65 100 75

A3 73 56 90 45

A4 74 100 75 80

A5 81 75 100 85

A6 79 80 62 88

A7 76 88 100 75

A8 82 100 95 77

A9 87 100 85 90

A10 77 65 80 85

A11 79 65 100 68

A12 80 75 100 78

A13 85 100 85 100

A14 76 75 61 70

Create a decision matrix (X) (figure 6).

[

81 55 60 80

77 65 100 75

73 56 90 45

74 100 75 80 81 75 100 85

79 80 62 88

76 88 100 75 82 100 95 77 87 100 85 90

77 65 80 85

79 65 100 68 80 75 100 78 85 100 85 100 76 75 61 70 ] Figure 6. Decision matrix

decision matrix normalization (X) step 1 and step 2 shown in figure 7 and table 11.

Figure 7. Matrix normalization step 1 Table 11. Matrix normalization step 2

Alternative

Criteria

C1 C2 C3 C4

A1 0,93 0,55 0,6 0,8

A2 0.89 0,65 1 0,75

A3 0,84 0,56 0,9 0,45

A4 0,85 1 0,75 0,8

A5 0,93 0,75 1 0,85

A6 0,91 0,8 0,62 0,88

A7 0,87 0,88 1 0,75

A8 0,94 1 0,95 0,77

A9 1,00 1 0,85 0,9

A10 0,89 0,65 0,8 0,85

A11 0,91 0,65 1 0,68

A12 0,92 0,75 1 0,78

A13 0,98 1 0,85 1

A14 0,87 0,75 0,61 0,7

Preference value ranking process (Vi ), enter the values from the normalization into the matrix.

The value of the ranking results can be seen in table 12

Table 12. Ranking

Alternative

Criteria

Results

C1 C2 C3 C4

A1 0,93 0,55 0,6 0,8 0,81

A2 0.89 0,65 1 0,75 0,84

A3 0,84 0,56 0,9 0,45 0,75

A4 0,85 1 0,75 0,8 0,87

(7)

Uniska PPJ-JST

A5 0,93 0,75 1 0,85 0,89

A6 0,91 0,8 0,62 0,88 0,85

A7 0,87 0,88 1 0,75 0,88

A8 0,94 1 0,95 0,77 0,94

A9 1,00 1 0,85 0,9 0,98

A10 0,89 0,65 0,8 0,85 0,83

A11 0,91 0,65 1 0,68 0,84

A12 0,92 0,75 1 0,78 0,88

A13 0,98 1 0,85 1 0,97

A14 0,87 0,75 0,61 0,7 0,81

Implements of management dialog

Login page, this page is the initial view of this system. In this view the admin can perform the login process. Figure 8 shows the login page.

Figure 8. Login Page Implementation

Home page, this page is the initial appearance of the admin homepage (figure 9).

Figure 9. Implementation of the admin home page

Santri Data Page

This page is a page that contains student data where the admin can add, view, change, and delete student data (figure 10).

Figure 10. Implementation of santri data pages

Achievement santri registration page, this page is a page for filling out the registration form for outstanding students (figure 11) where the admin can fill in the personal data form along with the student's grades according to existing criteria.

The admin can also add, view, modify, and delete student data.

Figure 11. Registration page implementation achievement students

Ranking page of students with achievement, this page is the results page for ranking students scores from each criterion that has been normalized (figure 12).

Figure 12. Implementation of achievement santri ranking pages

(8)

Uniska PPJ-JST Ranking report page, this page is a ranking report

page that can be used as a soft file (figure 13).

Figure 13. Report page implementation ranking

System testing shows the black box test (table 13).

Table 13. Black box test

No. Class Test

Scenario Test

Results Expected

Test result running Not

1 Login User

Leave the username

and password blank or one

of them

Please fill out first

usernames and the password is

filled with the wrong value

The system refuses to log

in

usernames and password is filled using the correct

value

Log into the system

2

Student data filling

Add student data

Log into the system Add student

data via excel

Log into the system

View student data

Can see the data in the system Changing

student data

Data can be changed Deleting

student data

Data can be deleted

3

Filling in Santri Value Data

Change the value data of

students

Data can be changed

Adding student value

data via excel

Data enters into the system View student

data

Can view data

4 Selected Students

The name of the selected student

The system displays the name of the selected

student

Conclusion

Decision Support System for Selection of Outstanding Santri Syaichona Moh Islamic Boarding School. Cholil Gambut has been successfully created so that the process of selecting outstanding students can take place quickly. The result of the user's calculation is the ranking of the highest value to the lowest and the highest value is the result needed as material for consideration by the user to determine the selection of outstanding students. From the calculation above, it can be concluded that the highest score with a value of 0.90 is the value recommended by the user to determine the outstanding students of grade 4 MI at the Syaichona Moh Islamic Boarding School. Cholil Gambut.

Compliance with ethical standards Conflict of interest

The authors declare that they have no conflict of interest.

References

Nurmalini and Robbi R. (2017). Study approach of simple additive weighting for decision support system. International Journal of Scientific Research in Science and Technology (IJSRS), 3(3).

Painem and Hari S. (2019). Decision support system with simple additive weighting for recommendation best employee. 6^th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI).

Limbong, T. (2002). Implementasi metode simple additive weighting (SAW) untuk pemilihan pekerjaan bidang informatika.

Evitasari. (2021). Sistem pendukung keputusan.

Guru akuntansi.co.id.

Adani, M. R. (2021). Penerapan sistem pendukung keputusan (SPK) dalam

(9)

Uniska PPJ-JST teknologi informasi. PT sekawan media

informatika.

MacCrimon, K. R. (1968). Decision Making Among Multiple Attribute Alternatives:

A survey and Consolidated Approach.

RAND, RM4823-ARPA.

Mulyati, S. (2016). Penerapan Metode Simple Additive Weighting Untuk Penentuan Prioritas Pemasaran Kemasan Produk Bakso Sapi. Jurnal Informatika, 1(1), 33- 37.

Permatasari, I. (2016). Sistem pendukung keputusan untuk menentukan jurusan pada smk islam kader bangsa

menggunakan metode SAW.

Repository. Nusamandiri. ac.id.

Dyah P., Juliana P., dan Dewi A. R. (2014).

Decision support system to majoring high school student using simple additive weighting method.

International Journal of Computer Trends and Technology, 10(3), 153-159.