<![CDATA[This research was conducted at the Modern Library of Kendari City to classify visitors' reading interests using the k-means clustering method. The classification aims to provide reading recommendations that match each visitor group's interests. The study uses book lending data collected from library visitors over the past four years. The clustering process implements the k-means algorithm, grouping data based on the nearest distance to cluster centers. This method resulted in three main clusters: cluster 0 with low reading interest, cluster 1 with moderate reading interest, and cluster 2 with high reading interest. This study contributes by developing a new approach for the Modern Library of Kendari City in managing book collections and recommending readings based on visitor interest groups. The clustering visualization provides insights into reading interest distribution, which helps the library make decisions about reading material provision. The cluster analysis shows different borrowing patterns and book preferences. This research is expected to help the library improve its services and visitor satisfaction through providing book collections that match each group's reading interests. Keywords – Book Recommendations; Clustering; Library; Machine Learning; Reading Interest]]>
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