Perbandingan K-Means dan K-Medoids untuk Analisis Perilaku Operasional Kunjungan Perpustakaan Sekolah Menengah Atas
DOI:
https://doi.org/10.47065/bulletincsr.v6i6.1412Keywords:
K-Means; K-Medoids; Patron Segmentation; Library Visits; Cluster ValidationAbstract
Library visit records provide information about patron behavior, yet aggregate counts do not explain differences in individual activity intensity and recency. This gap hampers senior high school library managers with limited staff in setting service priorities. This study compares K-Means and K-Medoids to analyze the operational visit behavior of the SMAN 48 Jakarta library. The dataset contains 10,050 transactions recorded between August 18, 2025 and October 7, 2026, represented as 1,033 anonymized visitor profiles. The study follows the CRISP-DM framework, constructs nine recency–frequency–temporal features, applies logarithmic transformation and Z-score standardization, and evaluates 14 configurations with two to eight clusters. K-Means with two clusters achieves the highest Silhouette score of 0.4114 and a Davies–Bouldin Index of 1.1651, whereas the best K-Medoids configuration uses five clusters with scores of 0.2825 and 1.2348. The selected partition contains 165 low-intensity visitors (15.97%) and 868 relatively more active visitors (84.03%). Ten initialization runs reproduce the membership exactly, while removing active days, which duplicates visit frequency, lowers the Silhouette to 0.3896 with an Adjusted Rand Index of 0.9705; the two-segment structure persists although part of its separation stems from double weighting. The novelty lies in an evaluation protocol for school library visit logs that combines a two-algorithm comparison, metric re-verification, initialization stability testing, and feature sensitivity analysis. Its practical contribution comprises a basis for peak-hour staff scheduling, a follow-up list of infrequent visitors, and a semester-based segment update cycle. The results describe recorded utilization and do not establish reading interest, satisfaction, or academic impact.
Downloads
References
S. Khademizadeh, Z. Nematollahi, and F. Danesh, “Analysis of book circulation data and a book recommendation system in academic libraries using data mining techniques,” Library & Information Science Research, vol. 44, no. 4, Art. no. 101191, 2022. doi: 10.1016/j.lisr.2022.101191.
R. O. Okunlaya, N. Syed Abdullah, and R. A. Alias, “Artificial intelligence (AI) library services innovative conceptual framework for the digital transformation of university education,” Library Hi Tech, vol. 40, no. 6, pp. 1869-1892, 2022. doi: 10.1108/lht-07-2021-0242.
K. M. Jones and L. J. Hinchliffe, “Ethical issues and learning analytics: Are academic library practitioners prepared?,” The Journal of Academic Librarianship, vol. 49, no. 1, Art. no. 102621, 2023. doi: 10.1016/j.acalib.2022.102621.
H. Abbasimehr and A. Bahrini, “An analytical framework based on the recency, frequency, and monetary model and time series clustering techniques for dynamic segmentation,” Expert Systems with Applications, vol. 192, Art. no. 116373, 2022. doi: 10.1016/j.eswa.2021.116373.
A. M. Ikotun, A. E. Ezugwu, L. Abualigah, B. Abuhaija, and J. Heming, “K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data,” Information Sciences, vol. 622, pp. 178-210, 2023. doi: 10.1016/j.ins.2022.11.139.
A. E. Ezugwu et al., “A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects,” Engineering Applications of Artificial Intelligence, vol. 110, Art. no. 104743, 2022. doi: 10.1016/j.engappai.2022.104743.
E. Schubert and P. J. Rousseeuw, “Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms,” Information Systems, vol. 101, Art. no. 101804, 2021. doi: 10.1016/j.is.2021.101804.
L. Lenssen and E. Schubert, “Medoid Silhouette clustering with automatic cluster number selection,” Information Systems, vol. 120, Art. no. 102290, 2024. doi: 10.1016/j.is.2023.102290.
C. Wongoutong, “The impact of neglecting feature scaling in k-means clustering,” PLOS ONE, vol. 19, no. 12, Art. no. e0310839, 2024. doi: 10.1371/journal.pone.0310839.
T. Ullmann, C. Hennig, and A. L. Boulesteix, “Validation of cluster analysis results on validation data: A systematic framework,” WIREs Data Mining and Knowledge Discovery, vol. 12, no. 3, Art. no. e1444, 2022. doi: 10.1002/widm.1444.
R. Todeschini, D. Ballabio, V. Termopoli, and V. Consonni, “Extended multivariate comparison of 68 cluster validity indices. A review,” Chemometrics and Intelligent Laboratory Systems, vol. 251, Art. no. 105117, 2024. doi: 10.1016/j.chemolab.2024.105117.
E. Schubert, “Stop using the elbow criterion for k-means and how to choose the number of clusters instead,” ACM SIGKDD Explorations Newsletter, vol. 25, no. 1, pp. 36-42, 2023. doi: 10.1145/3606274.3606278.
M. Gagolewski, “A framework for benchmarking clustering algorithms,” SoftwareX, vol. 20, Art. no. 101270, 2022. doi: 10.1016/j.softx.2022.101270.
I. Van Mechelen et al., “A white paper on good research practices in benchmarking: The case of cluster analysis,” WIREs Data Mining and Knowledge Discovery, vol. 13, no. 6, Art. no. e1511, 2023. doi: 10.1002/widm.1511.
G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning: With Applications in R, 2nd ed. Springer US, 2021. doi: 10.1007/978-1-0716-1418-1.
K. P. Murphy, Probabilistic Machine Learning: An Introduction. MIT Press, 2022. [Online]. Available: https://mitpress.mit.edu/9780262046824/probabilistic-machine-learning/
L. W. Yerbury, R. J. G. B. Campello, G. C. Livingston, Jr., M. Goldsworthy, and L. O’Neil, “On the Use of Relative Validity Indices for Comparing Clustering Approaches,” ACM Transactions on Knowledge Discovery from Data, vol. 19, no. 8, pp. 1-53, 2025. doi: 10.1145/3748726.
scikit-learn developers, “Clustering,” 2026. Accessed: Oct. 9, 2026. [Online]. Available: https://scikit-learn.org/stable/modules/clustering.html
F. Iglesias Vázquez and T. Zseby, “Temporal silhouette: validation of stream clustering robust to concept drift,” Machine Learning, vol. 113, no. 4, pp. 2067-2091, 2024. doi: 10.1007/s10994-023-06462-2.
American Library Association, “Library Privacy Guidelines for Library Management Systems,” 2022. Accessed: Oct. 9, 2026. [Online]. Available: https://www.ala.org/advocacy/privacy/guidelines/library-management-systems
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Perbandingan K-Means dan K-Medoids untuk Analisis Perilaku Operasional Kunjungan Perpustakaan Sekolah Menengah Atas
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2026 Rian Septian Anwar, Entin Sutinah, Nani Agustina
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
