Fuzzy C-Means Algorithm for Grouping Students Based on Preferences and Academic Potential - Urva, Gellysa; Desriyati, Welly (Sinkron : jurnal dan penelitian teknik informatika, 2025)
Personalized education is increasingly becoming a necessity in the modern era to ensure that students get a learning experience that is relevant to their interests and academic potential. This study aims to group students into three main clusters, namely Science, Arts, and Business, using the Fuzzy C-Means (FCM) algorithm. The FCM algorithm was chosen because of its flexibility in handling multidimensional data and allows students to have degrees of membership in more than one cluster, reflecting the multidisciplinary nature of their preferences. The research dataset consists of data on students' interests in fields of study (Science, Arts, Business) and academic grades in related subjects. The clustering results show that: The Business cluster includes 59 students (46.9%), reflecting the dominance of interests in economics, global trend analysis, and business organization activities. Artcluster consists of 39 students (30.0%), who show a preference for visual arts, art portfolio development, and involvement in community design. Science cluster has 30 students (23.1%) with interests in biology, science experiments, and biotechnology. Evaluation using Davies Bouldin Index (DBI) yields a value of 0.78, indicating good cluster quality. In addition, manual validation from teachers shows that more than 85% of students in each cluster fit the grouping based on direct observation. This study makes a significant contribution to the development of data-driven academic recommendation systems, enabling educational institutions to design learning programs that are more adaptive, relevant, and in accordance with student needs.