Implementasi K-Means Clustering guna Analisis Pola Minat Ekstrakurikuler Sebagai Dasar Rekomendasi Pengembangan Kegiatan Di SMP 2 Jati Kudus

Abstract

Abstract—This study implements the K-Means clustering algorithm to analyze students’ interest patterns in extracurricular activities at SMP 2 Jati Kudus. The dataset includes 18 extracurricular programs with features such as total registrants, accepted participants, pending status, and rejected applicants. The Elbow Method was applied to determine the optimal number of clusters, resulting in three clusters representing high, medium, and low interest groups. The clustering results show clear differentiation among programs based on registration volume and acceptance indicators. These findings can support schools in determining priority development strategies, including improving facilities for high-interest programs, enhancing promotion for medium-interest groups, and evaluating learning content for low-interest programs. The study demonstrates that data-driven clustering approaches can effectively support decision-making in extracurricular program planning.