Skip to content
Open access

Selecting the Best Students Using the Simple Additive Weighting Method Decision Support System

Aug 2026 · ICIT Journal · 0 citations · 16 references

TL;DR

The results indicate that the SAW method can provide an objective and effective selection process and is proven to be effective for application in a decision support system for selecting the best student.

Abstract

Selecting the best student is one form of appreciation implemented by An-Nawa Islamic Boarding School in Serang Regency to encourage students' motivation, achievement, and discipline. The current selection process is conducted manually, which may lead to subjectivity and inconsistencies in decision-making. This study aims to design and implement a Decision Support System (DSS) using the Simple Additive Weighting (SAW) method to assist in determining the best student objectively, transparently, and systematically. The research employed a quantitative approach using five evaluation criteria: moral character, participation in learning activities, academic performance, attendance, and responsibility. The data consisted of 30 students evaluated based on these criteria. The SAW method was applied through the stages of decision matrix construction, normalization, weighting, and preference value calculation to generate a ranking of alternatives. The results indicate that the SAW method can provide an objective and effective selection process. Alternative A28 (Mashadi) achieved the highest preference value of 1.00 and ranked first, followed by A6 (Retno Safitri) with a score of 0.97 and A19 (Hoirun Saban) with a score of 0.91. The implemented system assists school administrators in accelerating the selection process, reducing assessment subjectivity, and producing more accurate and accountable decisions. Therefore, the SAW method is proven to be effective for application in a decision support system for selecting the best student.

Read PDF

Similar papers

Open access Sep 2026

Implementation of a Decision Support System for High-Achieving Student Selection Using Simple Additive Weighting

A web-based decision support system using the Simple Additive Weighting method to rank 21 students based on academic performance, attendance, attitude, and participation, weighted 40%, 25%, 20%, and 15%, respectively, provides a structured and reproducible ranking aid based on school-defined criteria and weights.

P. Sari, Fauriatun Helmiah, Wan Mariatul Kifti · 0 citations

Penerapan Technique for Order Preference by Similarity to Ideal Solution

The research results demonstrate that the TOPSIS method is capable of producing a more objective, transparent, and measurable assessment process, thus assisting leaders in effectively identifying the best educational staff.

Dini Setyorini, Helina Apriyani · 0 citations
Open access Aug 2026

Implementation of a Decision Support System Based on the Simple Additive Weighting (SAW) Method and Apriori Algorithm for Student Achievement Evaluation

Test results show that both SAW and Apriori achieved an accuracy of 87% compared to manual calculations, indicating that the combination of these two methods can provide consistent and reliable decisions.

Mochammad Bagus Priyantono, Deni Mahdiana · 0 citations
Review Open access Sep 2026

Topsis-Based Decision Support System for Ranking Student Achievement

Determining outstanding students at Madrasah Manarul Huda has so far relied on homeroom teachers manually summing academic scores from the national Rapor Digital Madrasah (RDM) system with non-academic records – attendance, extracurricular activity, and competition achievement – without any criteria weighting, so the r...

Dea Setianawati, Niki Ratama · 0 citations
Open access Sep 2026

Sensitivity Analysis of a Simple Additive Weighting-Based Decision Support System for Strategic E-Learning Evaluation

This study designs a Decision Support System (SPK) using the Simple Additive Weighting method which is integrated with sensitivity analysis, and proves that the model not only functions as aranking tool, but also as a precise early warning system for university management to carry out data-driven preventive interventio...

A. Arribathi, Haerudin, Danang Rifai · 0 citations
Open access Sep 2026

Multi-Criteria Evaluation Of Student Career Readiness Using An Intelligent Topsis-Based Decision Support System

It is demonstrated that TOPSIS can effectively rank and classify student career readiness based on multiple assessment criteria and can support higher education institutions in identifying students’ readiness levels and developing more objective, targeted, and data-driven career development strategies.

Revi Gusriva · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.