Book Data Grouping in the Library Using the K-Means Clustering Method
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This study aims to classify the book information contained in the Business Indonesia Polytechnic library. Book grouping is done using the K-Means Clustering method. In this K-means clustering algorithm, the variables used as input are: book id, book title, total loan and copies. 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. 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 final results obtained consisted of: members of cluster 1 consisting of 119 book titles, cluster2 of 8 books, and cluster3 of 21 books. From the lending data, the data contained in cluster 3 is the book group with the highest loan amount among the other 2 clusters. In addition, books contained in cluster 3 have the fewest copies. From the results of this data analysis, it can be seen that the book titles contained in cluster 3 are the most recommended books to be added to the Business Indonesia Polytechnic library
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