Enhancing Scholarship Selection Process with a Simple Additive Weighting-Based Decision Support System

Abstract

Scholarships play a critical role in supporting students' educational pursuits, particularly those from financially disadvantaged backgrounds. The increasing number of applicants, however, poses challenges for fair and efficient scholarship selection. This study proposes a Decision Support System (DSS) utilizing the Simple Additive Weighting (SAW) method to streamline the scholarship recipient selection process. The system evaluates applicants based on seven criteria, including GPA score, SKKM (Student Activity Credit Unit), Total Parent's Income, Number of siblings, Status of Receiving Scholarship, Employment Status, Age. Data normalization was implemented to standardize criteria with varying scales, ensuring fairness and comparability. The system was tested on real-world data, demonstrating an effective ranking mechanism with high consistency compared to expert evaluations (Spearman’s rs=0.92). Key findings highlight the system's transparency, flexibility in adjusting weights, and efficiency in handling large datasets. This research contributes to the development of equitable scholarship distribution mechanisms by offering an objective, data-driven approach to decision-making. Future enhancements may include integrating machine learning techniques to improve predictive capabilities.