<![CDATA[The Universitas Pahlawan Library has utilized the Senayan Library Management System (SLiMS) in managing library activities, but the system does not yet provide a book procurement recommendation feature based on borrowing levels. This study aims to implement the K-Means Clustering algorithm to classify book borrowing data and design a book procurement recommendation feature in the SLiMS application. The method used in this study is Knowledge Discovery in Database (KDD), which includes data selection, preprocessing, transformation, data mining, and evaluation stages. The data used were book borrowing records totaling 1424 raw data entries, resulting in 594 clean data entries after preprocessing. The variables used include average borrowing duration, book category, and book stock. The Clustering process was carried out using the K-Means algorithm with three clusters, namely highly borrowed, moderately borrowed, and rarely borrowed books. The results showed that the K-Means algorithm successfully classified book borrowing data based on borrowing levels, which can be used as a basis for more targeted book procurement recommendations. In addition, this study produced a design for a book procurement recommendation feature in the SLiMS application to assist librarians in making library collection procurement decisions.]]>
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