Design of a Decision Support System for Assessing Student Indiscipline Using the Web-Based Simple Additive Weighting (SAW) Method

Abstract

Student discipline is crucial for a conducive learning environment, yet many schools still assess indiscipline manually, leading to inefficiency. This research develops a web-based decision support system using the Simple Additive Weighting (SAW) method to objectively assess and rank student violations. The system helps educators manage offense data, assign weights to different violation criteria, and issue appropriate sanctions. Built with the Laravel framework for security and scalability, the system was tested with 50 teachers and administrators. Evaluation focused on usability, satisfaction, and the system’s accuracy in ranking student indiscipline. Results showed that 85% of top-ranked students had frequent violations, while 90% of lowest-ranked students had few. The system enables teachers to review data, generate accurate reports, and issue warnings efficiently, reducing the time spent on manual assessments. However, it is currently limited to web platforms and lacks integration with mobile notifications like WhatsApp. Despite these limitations, the system significantly enhances discipline management and provides an objective, technology-driven solution for educational institutions