Advanced Journal of STEM Education, Vol. 3 No. 1 (2025)

https://doi.org/10.31098/ajosed.v3i1.3372
Research Paper

Design of a Decision Support System for Assessing Student Indiscipline
Using the Web-Based Simple Additive Weighting (SAW) Method
Susi Wagiyati Purtiningrum1, Yudi Irawan Chandra*2 , Dian Gustina1, Nafisah Yuliani1,
Fahrul Nurzaman1, Jhonny Z A1, Agus Wismo1, Sularso Budilaksono1
1 Universitas Persada Indonesia YAI, Indonesia
2 Sekolah Tinggi Manajemen Informatika dan Komputer, Indonesia

Received : January 15, 2025

Revised : May 19, 2025

Accepted : May 22, 2025

Online : May 31, 2025

Abstract
Student discipline is crucial for a conducive learning environment, yet many schools still assess indiscipline
manually, leading to inefficiency. This research develops a web-based decision support system using the Simple
Additive Weighting (SAW) method to objectively assess and rank student violations. The system helps educators
manage offense data, assign weights to different violation criteria, and issue appropriate sanctions. Built with
the Laravel framework for security and scalability, the system was tested with 50 teachers and administrators.
Evaluation focused on usability, satisfaction, and the system’s accuracy in ranking student indiscipline. Results
showed that 85% of top-ranked students had frequent violations, while 90% of lowest-ranked students had few.
The system enables teachers to review data, generate accurate reports, and issue warnings efficiently, reducing
the time spent on manual assessments. However, it is currently limited to web platforms and lacks integration
with mobile notifications like WhatsApp. Despite these limitations, the system significantly enhances discipline
management and provides an objective, technology-driven solution for educational institutions.
Keywords: Decision Support System, Student Indiscipline, Simple Additive Weighting (SAW), Web-Based
Application, Educational Technology

INTRODUCTION
Currently, the level of student discipline is a cause for concern, and not just a few occasional
violations committed by students over time; many violations committed by students can affect
their progress and achievement in class (Nasri & Usman, 2019; Student Discipline, n.d.). MA AlMuddatsiriyah assesses student indiscipline and determines warnings manually, or does not yet
have a special system for it. It can take a long time and is less than optimal. In that case, the existence
of a decision support system on student indiscipline assessment can help counseling guidance
teachers in determining undisciplined students and giving/determining sanctions given to
undisciplined students (Integrating a Decision Support System into a School, 2002). Using the SAW
method because the calculation is faster and can be used for solving decision-making systems by
considering criteria and weights for calculations to determine the assessment of student
indiscipline (Fitrul et al., 2019; Sanjaya & Narendra, 2023; Taherdoost, 2023).
The problem formulation of this research is how to design and build a web-based student
indiscipline assessment decision support system using the simple additive weighting (SAW)
method. The problems discussed are the student indiscipline assessment decision support system
is website-based and uses Laravel as a framework for program development; This system uses the
SAW (Simple Additive Weighting) ranking method to determine undisciplined students and
determine warnings for undisciplined students; This research was conducted in the MA AlMuddatsiriyah environment to obtain data as a reference in the research. The system built is
Copyright Holder:
© Susi, Yudi, Dian, Nafisah, Fahrul, Jhonny, Agus & Sularso. (2025)
Corresponding author’s email: yirawanc@gmail.com

This Article is Licensed Under:

Adv. J. STEM. Ed.

expected to support decisions for the determination of undisciplined student warnings, as a followup for undisciplined students, and assist counseling guidance teachers in assessing undisciplined
students.
LITERATURE REVIEW
Decision Support System
Decision Support Systems (DSS) are a subset of computer-based information systems
specifically designed to assist in managerial decision-making processes, particularly in situations
where decisions are semi-structured and require both analytical tools and human judgment. These
systems serve as supportive instruments that provide relevant data, modeling capabilities, and
interactive software to help decision-makers explore various alternatives and evaluate potential
outcomes. However, it is important to note that DSS are not intended to replace the intuition,
experience, or discretion of decision-makers, but rather to enhance and complement their decisionmaking capabilities (Chung et al., 2016; Hasan et al., 2017; Karo et al., 2024). A Decision Support
System (DSS) should not be misunderstood as merely providing support for decisions; rather, it is
a comprehensive system designed to transform raw data into meaningful information by applying
relevance-based processing. This information then serves as a foundation to assist decision-makers
in evaluating alternatives and selecting the most effective course of action. A well-developed DSS
enhances the speed and accuracy of problem-solving, enabling organizations to respond promptly
and effectively to various challenges (Abina et al., 2023; Peng & Pei, 2022).
Simple Additive Weighting (SAW)
The Simple Additive Weighting (SAW) method, often recognized as one of the most
straightforward and widely used techniques in multi-criteria decision-making (MCDM), is designed
to evaluate and rank alternatives based on a set of predefined criteria. Commonly abbreviated as
SAW, this method functions by calculating a total score for each available option. This score is
derived by summing the weighted performance values of each alternative across all relevant
attributes or criteria. Each attribute in the decision matrix is assigned a specific weight that reflects
its level of importance relative to the overall goal of the evaluation process.
To implement the SAW method, the decision matrix is first normalized to ensure that all values
are on a comparable scale. This is particularly important when the criteria involve different units
of measurement or value ranges. Once normalization is complete, each normalized value is
multiplied by the weight assigned to its corresponding criterion. The resulting values for each
alternative are then added together, producing a final score that represents the overall performance
of the alternative. The alternative with the highest total score is typically considered the most
preferred or optimal choice.
SAW is highly regarded for its simplicity and ease of application, making it suitable for a wide
range of decision-making scenarios, from business strategy selection to product evaluation and
resource allocation. Despite its simplicity, the method requires careful consideration when
assigning weights and selecting criteria to ensure the outcomes are both objective and reflective of
stakeholder preferences. Moreover, the SAW method assumes linear trade-offs between criteria,
which may not always represent complex real-world preferences but can provide a useful
approximation in many practical situations.
By employing SAW, decision-makers are equipped with a systematic and transparent
framework to compare multiple alternatives efficiently, based on quantifiable metrics aligned with
their goals (Eniyati, 2011; Sihombing et al., 2024). In this approach, it is essential to perform a
normalization process on the decision matrix (X) to ensure that the values representing various
alternatives are transformed onto a common scale. This step allows for fair and meaningful
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comparisons across all criteria and options being evaluated. The normalization can be carried out
using the following mathematical formula:

1)
where j is a benefit attribute and j is a cost attribute
The preference value (Vi) of each alternative is given by :

2)
Description:
Vi = Preference value
Wj = Rank weight
Rij = Normalized performance rating
A greater Vi score suggests that the alternative AI holds a higher level of preference
compared to the others. The Simple Additive Weighting (SAW) method involves several key steps
in its implementation process:
1) Identifying and listing all possible alternatives along with the relevant attributes or
evaluation criteria.
2) Assigning appropriate weights to each criterion based on its level of importance in the
decision-making context.
3) Ranking the alternatives by computing a weighted sum, ensuring that each criterion’s
weight accurately represents its significance and contributes proportionally to the final
evaluation, thereby reflecting both priority and comparative distance among alternatives.
Discipline
Discipline can be understood as a pattern of behavior that evolves through internal
development and is shaped by the need to align with established norms, regulations, and decisions.
The term "discipline" is derived from the Latin word discere, which means "to learn." Over time, the
concept of discipline has expanded and taken on multiple interpretations across various contexts.
In its most fundamental sense, discipline refers to the ability and willingness to adhere to rules,
demonstrate respect for authority, and submit to guidance and control. This perspective highlights
discipline as a response to external expectations and structured systems of oversight.
Another interpretation views discipline as a form of self-governance, a process through
which individuals cultivate personal orderliness, responsibility, and the capacity to act consistently
and ethically even in the absence of direct supervision. This understanding emphasizes discipline
as a tool for personal development, enabling individuals to act with purpose, manage impulses, and
maintain focus on long-term goals. It is closely linked with concepts such as self-motivation,
accountability, and perseverance.
Furthermore, discipline is often seen as an essential quality in professional and academic
environments, where individuals are expected to carry out their duties with integrity, punctuality,
and commitment. In this regard, discipline serves not only as a measure of compliance but also as
an indicator of one’s reliability and dedication to assigned responsibilities.

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Ultimately, discipline can be concluded as a manifestation of personal accountability. It
reflects a conscious choice to respect established standards and societal norms while fulfilling one’s
roles and responsibilities. Whether it stems from external enforcement or internal motivation,
discipline remains a foundational value that supports order, productivity, and ethical behavior in
both individual and collective settings (Beer et al., 2021; Minhua & Hock, 2024).
RESEARCH METHOD
Development Model
This research employed the Waterfall Model for system development (Chandra et al., 2022;
Dwivedi et al., 2022). Figure 1 depicts the sequential stages involved in the software development
process, which include the following:

Figure 1. Waterfall Model Activity Stage
Requirement Definition
The initial phase is where project needs are gathered and documented. Stakeholders define
functional and non-functional requirements through research, interviews, or surveys. In this
project, the system is being developed for MA Al-Muddatsiriyah. The output is a Software
Requirement Specification (SRS) document, serving as the foundation for the design, development,
and testing phases.
System and Software Design
This phase involves transforming requirements into a structured system architecture. It
includes high-level design (such as system structure and data flow) and detailed design (including
database schema, algorithms, and interfaces). In the development of the system at MA AlMuddatsiriyah, this stage ensures a well-defined blueprint before implementation, minimizing
errors and enhancing system efficiency.
Implementation and Unit Testing
Involves coding the system based on the design specifications. Developers write and
integrate modules, followed by unit testing to verify individual components. This ensures each unit
functions correctly before progressing to integration and system testing, maintaining a structured,
sequential development process. In this project, the system is being developed and implemented at
MA Al-Muddatsiriyah to meet the institution's specific requirements and improve its operational
efficiency.
Integration and System Testing
Integration and System Testing take place after the implementation phase, ensuring that
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individual modules function together as a complete system. This process includes integration
testing to identify interface issues and system testing to validate overall functionality. Methods such
as black-box testing are employed to verify compliance with design specifications and user
requirements. In the development of the system at MA Al-Muddatsiriyah, these testing phases play
a crucial role in ensuring reliability and effectiveness before deployment.
Operations and Maintenance
Involves the ongoing support and updates to the system after deployment. It focuses on
fixing issues, enhancing system performance, and ensuring the system operates smoothly. This
phase continues until the system is retired or replaced, ensuring long-term functionality.
Data Collection Methods
The following are the kinds of data that ought to be collected to carry out this research:
Observation
This method involves conducting direct observations at MA Al-Muddatsiriyah. By visiting
the location in person, researchers can collect firsthand information about the environment,
activities, and other relevant factors that contribute to the study. This approach allows for a more
comprehensive understanding of the research context through direct visual and contextual
analysis.
Interview
The interview was conducted by engaging with relevant sources at MA Al-Muddatsiriyah
to gather valuable insights and information.
Literature Study
This stage involves gathering relevant information regarding theories and research
findings within the same scientific field. The information is obtained from various sources,
including books, academic journals, and articles available on the internet. This process aims to build
a strong theoretical foundation and ensure a comprehensive understanding of the subject matter.
FINDINGS AND DISCUSSION
Problem Identification
MA Al-Muddatsiriyah serves as an educational institution that combines general and
Islamic studies at the high school level, with the primary mission of nurturing students' character
in alignment with the values and principles outlined in the Qur'an and Hadith. Despite its
commitment to moral and academic development, the institution currently faces challenges related
to student discipline. Instances of misconduct have been occurring with increasing frequency,
raising concerns among educators due to their adverse effects on students’ personal growth and
academic outcomes. At present, the school relies on a manual process to monitor disciplinary issues
and deliver warnings, as it lacks an integrated system to manage such matters. This conventional
method often proves inefficient, requiring considerable time and effort while yielding limited
results in promoting behavioral improvement.
Problem-Solving Identification
In response to the issues identified above, a comprehensive Student Indiscipline
Assessment Support System was designed and developed to assist in recognizing students
exhibiting undisciplined behavior. This system empowers the school administration to take timely
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and suitable actions to address these behaviors, including the issuance of formal warnings or other
corrective measures. To achieve this, the system utilizes the Simple Additive Weighting (SAW)
method, which facilitates the ranking of students based on the severity and frequency of their
indiscipline. This enables the school to assign appropriate warnings or interventions based on each
student's specific level of misbehavior. The system is implemented as a web-based platform,
utilizing the robust Laravel framework to ensure scalability and ease of access. The various criteria
considered for evaluating student indiscipline are detailed in Table 1, providing a clear and
structured approach to the assessment process.
Table 1. Criteria Categories
Criteria Category
Tardiness
Attendance
Clothing
Personality
Orderliness
Smoking
Ponography
Sharp Weapons
Drugs and Liquor
Fighting / Brawl
Intimidation/Threats with violence

Code
A
B
C
D
E
F
G
H
I
J
K

Score
5
5
5
8
8
10
10
10
10
10
10

Worship

L

9

Table 2 displays a comprehensive list of violations, which are categorized according to their
severity levels. These violations are integral components of the assessment criteria used in the
Student Indiscipline Assessment Support System, helping to evaluate student behavior effectively.
Each violation type is carefully classified to support a structured and systematic ranking process,
which is carried out using the Simple Additive Weighting (SAW) method. This approach ensures
that the severity of the violations is accurately considered, enabling the school to determine and
implement the most appropriate follow-up actions based on the rankings. By utilizing this method,
the system ensures a fair and consistent evaluation, promoting a structured approach to handling
student discipline.

Category

Tardiness

Attendance

Table 2. Type of Violation
Types of Violations
Late for the first hour after
5 minutes after the bell rings
Must follow tadarus every first hour
Late to the flag ceremony
Late entry after the break
Late for Zuhr prayer
Any absence without explanation
Absence by making false statements
Any skipping of class hours
Every absence from optional extracurricular activities without
explanation
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Score
2
2
2
2
3
5
3
3
5
20
10
20
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Category

Clothing

Personality

Order

Types of Violations
Score
Every absence from compulsory extracurricular activities without 3
explanation
Not wearing a uniform
10
Wearing the uniform is not neat / not inserted
5
Not wearing a ceremonial hat during the ceremony
2
Wearing sandals/slippers or shoes made into sandals
5
Not wearing plain black shoes
5
Wearing tight-fitting clothes (Crotch and Baggy)
20
Especially for girls wearing short sleeves and not wearing a
20
headscarfa hat that is not a school hat in the madrasah environment 3
Wearing
Wearing cutbray / torn pants or cingkrang pants
3
Do not wear OSIS / Location / name tag / Madrasah uniform /
5
Batik / Sports uniforms
Not wearing white socks 3/4 calf
2
Wearing a jacket/vest / sweater, unless sick with a doctor's
5
certificate
Wearing a belt that is not black/large
2
Not wearing a Muslim/Muslimah uniform on Friday
10
Wearing a Muslimah uniform is not by the madrasah regulations 3
Wearing a sports uniform when attending KBM instead of
Penjasorkes.
Excessive adornment for girls
Male students wear bracelets, necklaces, piercings, etc.
Male students have their hair covering their shirt collar/ears
Hair cut but not neat or with gel
Painted hair
Spewing obscene words among students
Saying obscene words in front of/in the hearing of parents
Hurting other people's feelings
Threatening fellow students/teachers
Stealing
Receiving guests during class without the teacher's permission in
the classroom
Entering
another class without the teacher's permission in the
classroom
Lying
to parents/teachers
Dating
Fighting parents/teachers

3

Coming home late without notification from the madrasah

25

Hanging out in a shop/Mall/Supermarket/Warnet/Game Center
while wearing a madrasah uniform
Defacing, scribbling on school property, teachers, employees,
friends, and others
Carrying tip ex
Damaging objects belonging to the school, teachers, and friends
Fighting with friends inside or outside the classroom
Making noise in the classroom when PBM is taking place

50

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2
3
4
2
5
4
2
5
25
100
10
10
20
20
50

50
2
50
25
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Category

Types of Violations
Jumping over the school fence to exit/enter

Score
10

Not carrying out K3 / Class Duty

25

Carrying a cell phone with a camera, without the permission of the 50
teacher, for the benefit of learning, as evidenced by a certificate
Activating cell phones during learning
25
Not reading the Qur'an during tadarus activities (instead of playing 10
around or doing homework during tadarus)
Doing activities outside of learning in the classroom (e.g., playing 10
ball, playing cards, throwing chalk)
Being outside the classroom during effective lessons
10
Disorderly during the ceremony

20

Leaving the class during the change of class hours, except with the 5
teacher's permission or to summon the teacher, by
Bringing cigarettes into school
10

Smoking

Smoking cigarettes in school/around school
Pornography
Sharp Weapons

Drugs and Liquor

Fighting

30

Bringing books, magazines, stencils, tapes, CDs, and pornographic 25
photos
Selling books, magazines, stencils, CDs, and pornographic photos 50
Viewing pornographic photos, tapes, and CDs
Carrying sharp weapons/fire without permission

25
100

Selling sharp/fire weapons
Using sharp weapons/fire to hurt others
Getting drunk at school
Bringing drugs/liquor to school

100
100
100
100

Using drugs, alcohol inside or outside school
Fighting/ brawl with other school students
Fighting between students/classes of MA. Al-Muddatsiriyah has a
wide impact
Fighting between students of the MA. Al-Muddatsiriyah has no
widespread
impact of fights
Being
a provocateur

100
100
100
50
50

Threatening and intimidating the principal, teachers, and
100
Intimidation/Threats Persecuting,
employees ganging up on the principal, teachers, and employees 100
with Violence
Being a provocateur against teachers, principals, and employees 100
Threatening and intimidating a student or group of students of MA. 100
Al-Muddatsiriyah
Disturbing a friend who is practicing worship
15

Worship

Teasing or intimidating friends of different religions

20

Calculation Process of Simple Additive Weighting Algorithm (SAW)
The following outlines the steps involved in the calculation of the Simple Additive
Weighting Algorithm, which utilizes the benefit formula. These steps are presented in detail in
Table 3, providing a clear and structured approach to the process.

Weight

2

Table 3. Data on the Number of Violations Committed
2
2
2
3
5
3
20
10

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Value
#
A1
A2
A3
A4
MAX

5%
c0
2
0
1
0
2

5%
c1
2
0
1
0
2

5%
c2
1
0
1
1
1

5%
c3
0
1
1
1
1

5%
c4
1
2
2
1
2

5%
c5
1
1
1
1
1

5%
c6
0
0
0
0
0

5%
c7
0
0
0
0
0

5%
c8
0
1
1
0
1

…

9%
c78
0
0
0
0
0

Based on the data presented in Table 3 above, the normalization process is performed by
dividing the value of each criterion for the alternative by the maximum value observed across all
alternatives. This approach ensures a standardized scale for comparison, as illustrated in the
subsequent Table 4 below.

#
A1
A2
A3
A4

c0
1
0
0,5
0

c1
1
0
0,5
0

Table 4. Normalization of Number of Violations
c2
c3
c4
c5
c6
c7
c8
1
0
0,5
1
0
0
0
0
1
1
1
0
0
1
1
1
1
1
0
0
1
1
1
0,5
1
0
0
0

…

c78
0
0
0
0

Table 5 presents the outcome of normalizing the total for each criterion, achieved by
dividing the sum of each criterion by its corresponding maximum value. This normalization process
ensures that all criteria are scaled proportionally, allowing for a more consistent and comparative
analysis across different parameters.

#
A1
A2
A3
A4

c0
2
0
1
0

c1
2
0
1
0

Table 5. Normalization with Weights
c2
c3
c4
c5
c6
c7
2
0
1,5
5
0
0
0
2
3
5
0
0
2
2
3
5
0
0
2
2
1,5
5
0
0

c8
0
10
10
0

…

c78
0
0
0
0

The values presented are the outcomes of multiplying the normalization process results by
the predefined weights, which are detailed in Table 4. This operation ensures that each factor's
significance, as determined in Table 4, is appropriately integrated into the final calculations
displayed in Table 6.

#
A1
A2
A3
A4

c0
0,1
0
0,05
0

c1
0,1
0
0,05
0

Table 6. Value Multiplication Result Data
c2
c3
c4
c5
c6
c7
c8
0,1
0
0,1
0,3
0
0
0
0
0,1
0,2
0,3
0
0
0,5
0,1
0,1
0,2
0,3
0
0
0,5
0,1
0,1
0,1
0,3
0
0
0

…

c78
0
0
0
0

Total
0,625
1
1,2
0,525

Table 7 is the result of the SAW (Simple Additive Weighting) calculation, where the results
of the multiplication in Table 6 are multiplied by the percent value in Table 4 above and produce a
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range of warnings that will be given to students who violate the predetermined points.
Table 7. Warning Range
Point
1-25
26-50
51-75

Warning
Warning I
Warning II
Warning III

Table 8 presents the final ranking results, which are derived from the calculations and
procedures outlined in Tables 4 through 7. These earlier tables detail the various steps and
methodologies used to arrive at the rankings, providing a comprehensive overview of the data
processing and analysis leading to the outcomes.

Alternative
A1
A2
A3
A4

Ranking
3
2
1
4

Table 8. Ranking Results
SAW
Point
0.625
18
1
23
1.2
29
0.525
12

Warning
Warning I
Warning I
Warning II
Warning I

System Flowchart Diagram
A flowchart diagram is a diagram that describes the workflow associated or a system being
created. This diagram can be used as a process or description in problem-solving. The following is
a flowchart diagram of the Decision Support System for Student Indiscipline Assessment with the
Simple Additive Weighting (SAW) Method shown in Figure 2.

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Figure 2. Flowchart Diagram of SAW Calculation
System Database Design
The design of system databases is one example of data modelling. The process of data
modelling teaches system builders about the flow of data and the contents of database table
columns. Figure 3 depicts the design of the database system that is based on the Physical Data
Model.

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Figure 3. Physical Data Model Database System
Hardware and Software Requirements
In order to put the newly developed system into operation, a number of components,
including hardware and software, are utilised.
Hardware
Hardware generates data, programmes, and outputs. A server supporting this application
has these hardware specs:
a. Processor: Intel Core i7
b. Memory: 16.0GB Dual-Channel
c. Hard Disk: 2794GB Seagate and 931GB Seagate
d. Monitor: 1920x1080 pixels
e. Keyboard 108 keys
f. Mouse: Optic PS/2
Software
In developing this application, the author uses software with the following specifications:
a. Operating System: Windows 11
b. Programme Package:
• XAMPP (Apache, MySQL)
• Bootstrap 5.0
• Laravel 10
• Notepad++

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•

Google Chrome, Mozilla Firefox, Mozilla Firefox Developer.

System Implementation Results
As may be seen in Figure 4 below, the following are some of the implementation
achievements that have been achieved in the form of page views on the web:

Figure 4. Application Display Results in Internet Browser
The system undergoes rigorous testing to verify its functionality. This research utilizes
black-box testing, a method focused on evaluating the system based on its inputs and expected
outputs. The test scenarios are designed using this approach, and the results are presented in Table
9.
Table 9. Application Trial Results
No Test
Testing Results
1
The user successfully registers and logs into the system using the account that has
been created.
2
Administrators can enter student violation data with various predefined categories.
3
The system successfully manages criteria weights to customize the Simple Additive
Weighting (SAW) method.
4
The system calculates student violation rankings with the SAW method accurately
based on the given criteria weights.
5
The system displays a ranking list of students who commit violations from highest to
lowest.
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No Test
6
7
8
9
10

Testing Results
The system successfully provides warning notifications to students who have a
certain level of offense
Administrators, teachers, and school staff can access the features according to their
access rights.
The system can generate student offense reports in PDF and CSV formats for school
needs.
The system can be accessed well on various devices such as desktops, tablets, and
smartphones.
Data entered into the system remains secure and does not suffer from loss or
unauthorized manipulation

CONCLUSIONS
The design of a Decision Support System (DSS) for assessing student indiscipline using the
Web-Based Simple Additive Weighting (SAW) method at MA Al-Muddatsiriyah has proven to be an
effective approach in addressing the challenges associated with managing student behavior. The
system integrates a user-friendly interface with the SAW method, which facilitates the objective
assessment of student discipline based on various predefined criteria. By automating the decisionmaking process, the system improves efficiency and accuracy, allowing administrators and
educators to make informed decisions regarding student disciplinary actions.
The use of the SAW method ensures that all relevant factors are considered and weighted
according to their importance, providing a comprehensive and fair evaluation of student behavior.
This approach minimizes human bias and subjectivity in the assessment process, promoting
transparency and fairness. Additionally, the web-based nature of the system makes it accessible
from various devices, offering flexibility to users.

LIMITATION & FURTHER RESEARCH
For further improvement, it is suggested that the system be expanded to include a feedback
mechanism where students can review and discuss the assessment results. This will promote
engagement and allow students to learn from the feedback provided. Moreover, incorporating
machine learning algorithms could enhance the system's ability to predict future student behavior
based on historical data, providing proactive solutions for discipline management.
Further research could explore the integration of additional methods of decision-making,
such as Multi-Criteria Decision Analysis (MCDA), to compare and enhance the SAW method's
effectiveness in evaluating student indiscipline. Investigating the system’s application in different
educational settings, as well as its potential for scalability to larger institutions, could provide
valuable insights into its versatility and impact. Additionally, studying the impact of the DSS on
student behavior and academic performance would be beneficial to determine the long-term effects
of such systems in educational environments.
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