K-Means Clustering untuk Analisis Tren Peminjaman Buku di Perpustakaan - Rosiana, Lidya; Yuadi, Imam (Journal of Technology and Informatics (JoTI), 2025)
This study aims to analyze book borrowing trends in libraries using the K-Means Clustering algorithm in Orange Data Mining. The data used in this research includes historical book borrowing records, such as borrowing frequency, book categories, and borrowing times. The study clusters the data to identify significant patterns and trends. The analysis process begins with data preprocessing, including data cleaning, normalization, and transformation. Subsequently, the K-Means algorithm is applied to divide the data into several clusters based on similarities in borrowing patterns. The results show that books in certain categories, exhibit distinct borrowing patterns. The generated clusters provide insights into the characteristics of groups of book titles with high borrowing intensity and book titles that tend to be borrowed at specific times. These insights can be utilized for more effective book collection management, the development of library promotion strategies, and the creation of book recommendation systems. This study demonstrates that the K-Means Clustering algorithm is an effective tool for library data analysis, enabling libraries to understand user needs and improve the services they provide.