Implementation of the Simple Additive Weighting Method in Determining Favorite Lecturers Based on Student Preferences

This study aims to develop a decision support system for evaluating and ranking favorite lecturers based on student preferences in higher education institutions. In the context of modern education, the role of lecturers goes beyond delivering lectures; they also serve as mentors and inspirations for students. However, the process of evaluating lecturer performance is often subjective and lacks structure, especially when it involves student preferences and perceptions of lecturers. Therefore, this study proposes the Simple Additive Weighting (SAW) method as a solution to this problem. By collecting data through Likert scale-based questionnaires, this research evaluates lecturer performance based on four main aspects: Pedagogical, Professionalism, Personality, and Social Interaction. Based on the calculation results using SAW, it was found that the best alternative is Alternative A2, which scored the highest total with 0.997. This indicates that lecturers associated with this alternative received high ratings in all aspects assessed by students. This conclusion provides a clear insight for educational institutions to improve educational management and enrich student learning experiences.