Decision Support System Modeling for Determining Thesis Advisors Using Profile Matching

Abstract

This study aims to develop a decision support system based on the Profile Matching method to provide automatic, objective, and systematic recommendations for determining thesis advisors. The background of this research is the advisor selection process that is commonly conducted manually by students, which often leads to an imbalance in lecturer workload and incompatibility between students’ research topics and lecturers’ areas of expertise. Such conditions can reduce the effectiveness of the thesis supervision process. The proposed system applies the Profile Matching method to compare lecturer competency profiles with student research needs by analyzing several criteria. These criteria are divided into Core Factors and Secondary Factors, which include the suitability of research fields, supervisory experience, and the number of students currently being supervised. Data used in this study were collected from students and lecturers in the Computer Science study program. The system’s recommendations were then tested and validated through manual calculations to evaluate their accuracy and reliability. The results of this study indicate that the developed decision support system is capable of generating an advisor ranking that aligns well with students’ research interests while also distributing supervision workloads more evenly among lecturers. Overall, this research demonstrates that implementing a decision support system using the Profile Matching method can enhance objectivity, fairness, and efficiency in determining thesis advisors. Furthermore, the system can serve as a foundation for the future development of academic recommendation systems in higher education institutions.