Web-Based Student Learning Style Decision Support System Using the WASPAS Method

Abstract

Determining students' learning styles is an essential aspect of optimizing the information absorption process in learning activities. However, the learning style identification process at SMP Fullday Al-Muhajirin Purwakarta is currently still carried out through manual recapitulation which takes time and has the potential to cause subjective bias in decision making by Guidance and Counseling (BK) teachers. This research provides a solution by developing an integrated website-based Decision Support System (DSS) using the Weighted Aggregated Sum Product Assessment (WASPAS) method. The aim is to automate the classification of students' learning styles into Visual, Auditory, or Kinesthetic (VAK) categories quickly and precisely. The software engineering method used is Prototype, which focuses on iterating user needs directly. This system was built using the Laravel 10 framework and MySQL database. The results of this research prove that the WASPAS method is able to process questionnaire instrument data into a stable decision matrix, producing structured ranking accuracy. Software testing shows that all application modules run smoothly, so this system has practically succeeded in accelerating the duration of identifying student learning styles and assisting BK teachers in designing targeted academic approach strategies.