A Web-Based Decision Support System for Determining High-Achieving Students Using The Simple Additive Weighting Method at SMK Kanisius Ungaran (Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika), 2026)
This study develops a web-based Decision Support System (DSS) to assist in determining academically high-achieving students at SMK Kanisius Ungaran. The current evaluation process in the school relies largely on manual assessment, which can make the management of multiple evaluation criteria time-consuming and difficult to organize systematically. To support a more structured evaluation process, this research applies the Simple Additive Weighting (SAW) method as a multi-criteria decision-making approach. Four assessment criteria were used in the system: report card average scores, school examination results, non-academic achievements, and attendance. Each criterion was assigned a weight based on institutional priorities. The system was implemented as a web application using Next.js and React.js for the front-end interface, while Supabase with PostgreSQL was used for data storage and management. The SAW procedure integrated into the system includes score normalization, weighted aggregation, and the generation of ranking results for students. A sample dataset consisting of five student alternatives was used to demonstrate the calculation process and system functionality. The results show that the system can process student evaluation data and generate ranking outputs based on the predefined criteria and weights. In the calculation example, the highest-ranked student obtained a final score of 0.9902. The developed system demonstrates how the SAW method can be operationalized within a web-based platform to support the organization and processing of multi-criteria student evaluation data. The study primarily contributes a practical implementation of a DSS for academic assessment in vocational secondary education contexts.