Implementation of a Decision Support System for High-Achieving Student Selection Using Simple Additive Weighting
TL;DR
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.
Abstract
Identifying high-achieving students requires a transparent and consistent assessment across academic and non-academic criteria. At SMK Swasta Siti Banun Sigambal, selection is still conducted manually and has not used explicit criterion weights. This study develops a web-based decision support system using the Simple Additive Weighting (SAW) method to rank 21 students based on academic performance, attendance, attitude, and participation, weighted 40%, 25%, 20%, and 15%, respectively. SAW normalizes benefit criteria, multiplies the normalized values by criterion weights, and sums them to obtain preference scores. Functional testing was conducted using eight Black Box Testing scenarios. The results ranked A19 first (1.00), A12 second (0.97), and A16 third (0.96), and all tested functions operated as expected. The system therefore provides a structured and reproducible ranking aid based on school-defined criteria and weights. However, the ranking remains dependent on the selected criteria and weights, and sensitivity analysis was not performed.