Supporting Academic Library Collection Decisions Using K-Means–Based  Book Recommendation

Wahyuni Edsa Safira; Saif Mohammed

doi:10.66053/aieds.v1i2.25

Authors

Wahyuni Edsa Safira Universitas Negeri Makassar
Saif Mohammed Bilkent University

DOI:

https://doi.org/10.66053/aieds.v1i2.25

Keywords:

Academic library management, Book recommendation system, Data mining, K-Means clustering, Library collection management

Abstract

Purpose - This study aims to develop a data-driven book recommendation system to support academic library collection management using the K-Means clustering method.
Methods - The study utilized book borrowing data from the Library of the Department of Informatics and Computer Engineering at Makassar State University collected over a 22-month period. Borrowing records were grouped by book categories and monthly borrowing frequencies, then processed into numerical variables. The K-Means algorithm was applied to identify borrowing pattern clusters, and cluster quality was evaluated using the Silhouette Coefficient to assess cohesion and separation.
Findings - The analysis produced three distinct clusters representing different borrowing behaviors. Programming and information technology books formed the most frequently borrowed cluster, research methodology books showed increased demand during specific academic periods, and education and learning methods books exhibited relatively lower borrowing intensity. The average Silhouette Coefficient value of 0.35 indicates a moderate yet acceptable clustering structure for recommendation and managerial purposes.
Research limitations - This study is limited to historical transaction data from a single departmental library and does not incorporate user profiles or qualitative preference data, which may restrict generalizability to other academic library contexts.
Originality - This study contributes empirical evidence on the use of K-Means clustering for book recommendation and decision support in academic libraries, demonstrating how borrowing pattern analysis can inform data-driven collection management and improve the relevance of library services. The findings also highlight the practical role of clustering analytics in supporting efficient resource allocation and evidence-based planning within higher education libraries and departmental level strategic decisions.

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