Mapping the Potential of Prospective New Students Utilizing K-Means and Fuzzy C-Means Clustering - Ahmad Fathoni; Eri Zuliarso; Sarah Husain Toman (International Journal of Social Learning (IJSL), 2025)
UIN Walisongo Semarang has implemented outreach initiatives to engage prospective students, but these endeavors often lack data-informed approaches. This research intends to analyze the potential of incoming students through K-Means and Fuzzy C-Means (FCM) clustering techniques. The dataset comprises admission information from 2022 to 2024, focusing on five key factors: gender, admission pathway, study program, school type, and geographic origin. Data preprocessing was performed before conducting the clustering analysis. The Elbow Method and Silhouette Score were utilized to identify the optimal K for K-Means, whereas the Fuzzy Partition Coefficient and Xie-Beni Index were applied for FCM. Findings indicate that K-Means generated more distinct cluster boundaries, while FCM provided adaptability with overlapping clusters. Principal Component Analysis and the Davies-Bouldin Index were employed to facilitate the assessment. The mapping results are displayed by faculty, showcasing regional patterns and student demographics. This research establishes a data-driven basis for UIN Walisongo's strategic recruitment and admissions strategies.