ANALISIS KOMPARATIF METODE AHP-TOPSIS DAN AHP-SAW DALAM SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN SISWA TERBAIK

Abstract

A Decision Support System (DSS) that leverages computer technology and analytical methods can aid decision-making in complex situations. This research aims to develop a DSS for selecting the best student in a school by comparing the performance of the AHP-TOPSIS and AHP-SAW methods. The study is motivated by the current manual selection process for the best student, which is prone to subjectivity, lacks a structured assessment system, and is time-consuming due to the large volume of student data. The research employs the waterfall method, which includes the stages of requirement analysis, design, implementation, and testing. The data used in this study includes report card grades, ethics scores, attendance records, and extracurricular activity scores from 45 students. The AHP method is applied to determine the weights of each criterion. Subsequently, TOPSIS and SAW are used to rank the students based on the score of similarity to the ideal solution (TOPSIS) or the weighted sum score (SAW). The results indicate that AHP-SAW (73.33% accuracy) outperforms AHP-TOPSIS (53.33% accuracy) in achieving consistency with manual calculation results. Using AHP-SAW on 45 data points resulted in 11 instances of rank differences, whereas AHP-TOPSIS resulted in 34 instances of rank differences. These findings suggest that AHP-SAW is more suitable for implementation in a DSS for selecting the best student due to its weighted summation approach, which is more aligned compared to AHP-TOPSIS that relies on the concept of distance to an ideal solution. This research contributes to a more efficient, objective, and accurate system for the best student selection process, enabling future decisions by schools to be based on this method.