From the lending data, the data contained in cluster 3 is the book group with the highest loan amount among the other 2 clusters. The final results obtained consisted of: members of cluster 1 consisting of 119 book titles, cluster2 of 8 books, and cluster3 of 21 books. With the use of the K-means clustering method, the final results of the grouping are obtained up to the 6th iteration, where the center point no longer changes and no data moves between clusters. The resulting output consists of 3 clusters, namely books that are borrowed most frequently, books that are borrowed frequently, and books that are rarely borrowed. In this K-means clustering algorithm, the variables used as input are: book id, book title, total loan and copies. Book grouping is done using the K-Means Clustering method. ![]() This study aims to classify the book information contained in the Business Indonesia Polytechnic library.
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