ANALISIS PROFILING KINERJA DOSEN BERBASIS ALGORITMA CLUSTERING: STUDI KASUS DATA EVALUASI MAHASISWA (EDOM) - Heri Subangkit; Taqwa Hariguna; Dhanar Intan Surya Saputra (Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, 2026)
Lecturer quality is a key factor determining the success of a higher education institution. Although EDOM assessment is crucial for performance, large data processing often does not provide sufficient strategic information. This study utilizes a no-learning method to profile lecturer performance. K-Means and Fuzzy C-Means (FCM) are two clustering algorithms that are compared with four competency variables. These variables are pedagogical, professional, social, and personality. The results show that there are three ideal clusters (k = 3) which are categorized as "Very Good", "Good", and "Fair" performance groups, respectively. The K-Means algorithm produces a Silhouette Score of 0.507. However, FCM is more flexible in determining the number of data transition members required. The profiling results show that pedagogical competence is the variable with the lowest score in the "Fair" cluster. The findings of this study suggest that, to improve the quality of educational services, lecturers in the cluster need to undergo training related to teaching methods.