<![CDATA[The Opera Reading Corner Library has ±600 book titles in several categories. In order for the books in the library to be utilized optimally there must be a system that manages the collection of books because from the process of selecting a collection of books it can be seen which books should be added or reduced in stock so as not to build up. stock.K-means Clustering is one of the methods in Data mining to process non hierarchical Clustering data where data is grouped into one or more Clusters and is one of the methods carried out with a partition system. This study classifies book reader data into two Clusters, namely high reading interest and low reading interest. Attributes used in[A1]Data processing includes the amount of book borrower data and the number of books borrowed. The results obtained for the book category data that are in the most desirable Cluster, this will later be used as evaluation material for the librarian in increasing the collection of books in the Opera Reading Corner library.]]>
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