<![CDATA[Library visit records provide information about patron behavior, yet aggregate counts do not explain differences in individual activity intensity and recency. This gap hampers senior high school library managers with limited staff in setting service priorities. This study compares K-Means and K-Medoids to analyze the operational visit behavior of the SMAN 48 Jakarta library. The dataset contains 10,050 transactions recorded between August 18, 2025 and October 7, 2026, represented as 1,033 anonymized visitor profiles. The study follows the CRISP-DM framework, constructs nine recency–frequency–temporal features, applies logarithmic transformation and Z-score standardization, and evaluates 14 configurations with two to eight clusters. K-Means with two clusters achieves the highest Silhouette score of 0.4114 and a Davies–Bouldin Index of 1.1651, whereas the best K-Medoids configuration uses five clusters with scores of 0.2825 and 1.2348. The selected partition contains 165 low-intensity visitors (15.97%) and 868 relatively more active visitors (84.03%). Ten initialization runs reproduce the membership exactly, while removing active days, which duplicates visit frequency, lowers the Silhouette to 0.3896 with an Adjusted Rand Index of 0.9705; the two-segment structure persists although part of its separation stems from double weighting. The novelty lies in an evaluation protocol for school library visit logs that combines a two-algorithm comparison, metric re-verification, initialization stability testing, and feature sensitivity analysis. Its practical contribution comprises a basis for peak-hour staff scheduling, a follow-up list of infrequent visitors, and a semester-based segment update cycle. The results describe recorded utilization and do not establish reading interest, satisfaction, or academic impact.]]>
