Penerapan Algoritma K-Means Clustering untuk Mengetahui Pola Peminjaman Buku di Perpustakaan Universitas Imelda Medan

Abstract

The use of a modern library management system is an important aspect in improving the quality of library services. The Imelda University Library in Medan has been using SLiMS 7 as its collection management and book borrowing transaction information system. This study aims to analyze borrowing data from SLiMS 7 and identify book borrowing patterns using the K-Means Clustering algorithm. The research data includes book categories, borrowing frequency, borrower study programs, and borrowing periods extracted directly from the SLiMS 7 database. The research stages include data pre-processing, normalization, determining the optimal number of clusters using the Elbow method, and the clustering process using the K-Means algorithm. The results of the study show the formation of several clusters that describe different borrowing patterns, such as clusters of collections with high, medium, and low borrowing rates. These findings can assist libraries in making decisions related to collection development, service organization, and book acquisition strategy planning. Thus, the integration of SLiMS 7 data with the K-Means algorithm has proven effective in generating information that supports the improvement of data-based library services.