Learning Evaluation System Using Fisher–Yates Shuffle and Rule-Based Algorithms at SMK Kahuripan 1

Abstract

SMK Kahuripan 1 faces challenges in conventional learning evaluation, particularly lengthy assessment processes and the potential for subjective evaluation. This study aims to develop a web-based learning evaluation system for Light Vehicle Engine Maintenance (PMKR) by integrating the Fisher–Yates Shuffle and Rule-Based algorithms. The system was developed using the Waterfall model, while the Fisher–Yates Shuffle was applied to randomize question sequences and the Rule-Based System with Forward Chaining was used to automatically evaluate learning outcomes based on predetermined criteria. The results show that the developed system successfully supports question randomization, automated scoring, learning outcome evaluation, and reporting. Black Box Testing confirmed that the main system functions, including login, dashboard access, question bank management, examinations, result reporting, and practice simulations, operated according to the specified requirements. Therefore, the system provides a more efficient, objective, and structured learning evaluation process.