Mapping the Potential of Prospective New Students Utilizing K-Means and Fuzzy C-Means Clustering - Ahmad Fathoni; Eri Zuliarso; Sarah Husain Toman (Indonesian Journal Publisher in cooperation with Indonesian Social Studies Association (APRIPSI), 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.