Application of Book Clustering Based on Borrowing Frequency Using K-Means at PBD Aviation Vocational School
DOI:
https://doi.org/10.35335/jict.v16i2.267Keywords:
Borrowing Frequency, Data Mining, K-Means Clustering, Library Management, Web-Based SystemAbstract
The rapid advancement of science and technology has enabled the application of data mining techniques in various domains, including education and library management. At PBD Aviation Vocational School, managing the library collection presents a major challenge due to the large number of students and the diverse frequency of book borrowing, leading to inefficiencies in procurement and maintenance. This study aims to develop a data-driven system for clustering books based on borrowing frequency using the K-Means algorithm to optimize collection management. The research was conducted using a quantitative approach, where library borrowing data were processed through the K-Means clustering method integrated into a web-based system. The algorithm classified books into three categories: frequently borrowed, quite frequently borrowed, and rarely borrowed. The results showed that the implementation of the K-Means algorithm effectively identified borrowing patterns, enabling the library to make more accurate decisions regarding book procurement, maintenance, and collection renewal. Furthermore, the web-based interface facilitated faster access and real-time visualization of borrowing trends, improving operational efficiency and data-driven decision-making. The findings highlight the importance of integrating data mining methods in educational library systems to enhance resource utilization and service quality, supporting evidence-based management in the digital transformation era.
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