Implementation of Data Mining for Mapping Student Visiting Interest Based on Study Programs as an Effort to Optimize Library Services at STMIK Methodist Binjai - Anzas Ibezato Zalukhu; Adil Priman Hati Hulu (SEAN Institute, 2026)
This study successfully implemented the K-Means Clustering algorithm to map students’ library visit interest patterns at the STMIK Methodist Binjai Library. The clustering results were empirically validated using a Silhouette Coefficient score of 0.8304, with the optimal number of clusters determined as k = 3 through the Elbow Method. The clustering process identified three strategic profiles: a Low-Interest Cluster consisting of 117 students with an average of 1.50 visits, a Moderate-Interest Cluster comprising 19 students with an average of 9.32 visits, and a High-Interest Cluster including 8 students with an average of 21.00 visits. The data analysis revealed significant disparities in visit interest across academic programs, where students from the Informatics Engineering program demonstrated higher levels of engagement compared to those from the Information Systems program, which was predominantly characterized by low visit frequency. These findings provide a scientific foundation for library management to formulate segmented service optimization policies, including retention programs for active users and personalized literacy stimulation strategies to enhance student engagement in academic programs with lower visit intensity