Abstract
Efficient allocation of academic supervisors is a critical yet challenging process in higher education, often hindered by mismatches in expertise and uneven workload distribution. This study introduces a web-based recommendation system leveraging the C4.5 decision tree algorithm to address these issues. By assessing supervisor expertise, workload, and alignment with student research topics, the system generates data-driven, accurate recommendations. Developed using the Laravel framework and the waterfall development model, the system emphasizes scalability and modularity. Functional testing demonstrated a 96.7% accuracy rate for recommendations, while usability testing reflected high user satisfaction, with an average score of 92% for ease of use and relevance. These results underscore the system’s effectiveness in optimizing supervisor assignments, enhancing administrative efficiency, and providing a scalable solution for educational institutions. Future work will focus on integrating diverse data sources and AI-driven features to further improve adaptability and responsiveness to evolving academic demands.
Published Version
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