<![CDATA[Introduction: Effective university admissions strategies require a clear understanding of applicant demographics, academic characteristics, geographic origins, and information channels. This study applies a hybrid clustering approach to identify meaningful applicant segments that can support targeted recruitment and resource allocation. Method: Historical applicant records from Universitas Sahid Surakarta covering 2021–2025 were preprocessed through data cleaning, one-hot encoding of categorical variables, and Min-Max normalization. A hybrid Self-Organizing Map (SOM) and K-Means framework was employed, where SOM projected high-dimensional applicant characteristics into a lower-dimensional topological representation and K-Means partitioned the resulting prototypes into distinct clusters. Clustering quality was evaluated using the Davies–Bouldin Index (DBI) to determine the optimal number of segments. Results and Discussion: The lowest DBI was obtained for three clusters, indicating the most appropriate segmentation structure. The resulting groups were characterized as proximity-driven local applicants, regional career-oriented applicants dominated by vocational-school backgrounds, and high-achieving out-of-region applicants with stronger academic performance and greater reliance on institutional websites and search channels. These patterns provide actionable insight for differentiated recruitment strategies. Conclusion: The hybrid SOM–K-Means approach effectively identifies interpretable applicant segments and provides descriptive intelligence that can support more targeted marketing, channel selection, scholarship strategies, and admissions resource allocation in higher education]]>
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