Segmentasi Perilaku Pemustaka Menggunakan DBSCAN untuk Optimalisasi Layanan Perpustakaan Digital
-
Candra Naya
*
Universitas Pelita Bangsa, Bekasi,
Indonesia
- Ermanto Ermanto Universitas Pelita Bangsa, Bekasi, Indonesia
- Unggul Prima Dhani Universitas Pelita Bangsa, Bekasi, Indonesia (*) Corresponding Author
Abstract
The rapid growth of digital libraries has generated increasingly large borrowing transaction data, creating the need for analytical techniques to understand user behavior patterns and support data-driven library management. This study aims to cluster library users based on their borrowing behavior using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to identify user segments according to their borrowing activity. The study employed the Book-Crossing Dataset, consisting of 278,858 rating transactions, 271,379 user records, and 271,360 book records. The research methodology included Exploratory Data Analysis (EDA), data preprocessing, feature engineering, feature standardization using StandardScaler, ε parameter selection through the K-Distance Graph, DBSCAN clustering, cluster evaluation using the Silhouette Score, and visualization using Principal Component Analysis (PCA). The experimental results indicate that ε = 0.5 and MinPts = 5 produced three clusters, with 244 users identified as noise. The obtained Silhouette Score of 0.5996 demonstrates a reasonably good clustering quality. Furthermore, the resulting clusters successfully represent users with low, moderate, and very high borrowing activities, providing valuable insights for developing personalized library services, improving book recommendation systems, supporting collection development, and facilitating data-driven decision-making to enhance the overall quality of digital library services.
Downloads
References
N. Vitriana, “Transformasi perpustakaan di era digital native,” Librarium: Library and Information Science Journal, vol. 1, no. 1, pp. 59–69, Mar. 2024, doi: 10.53088/librarium.v1i1.693.
A. F. Fiqori, I. Irawati, U. Faruq, and A. Fahriza, “Transformasi Digital dalam Pengelolaan Perpustakaan di SMA Negeri 5 Pekanbaru,” Social Science Academic, vol. 4, no. 1, pp. 183–192, May 2026, doi: 10.37680/ssa.9667.
B. S. Nahar, “Digital Transformation in Library Science: Enhancing Access, Preservation, and User Engagement,” Journal of Advanced Research in Library and Information Science, vol. 12, no. 4, pp. 14–18, Dec. 2025, doi: 10.24321/2395.2288.2025012.
K. Azzahra and E. Rahmah, “Strategi Pustakawan dalam Menghadapi Pemustaka Generasi Z di Perpustakaan Universitas Negeri Padang,” Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence), vol. 5, no. 1, pp. 94–100, Apr. 2025, doi: 10.55382/jurnalpustakaai.v5i1.957.
E. Utama, J. Junaidi, and Z. Zamzami, “Pengaruh Kompetensi Pustakawan Terhadap Kepuasan Pemustaka dengan Fasilitas Perpustakaan Sebagai Variabel Intervening,” Jurnal Pustaka Budaya, vol. 13, no. 1, pp. 85–105, Jan. 2026, doi: 10.31849/avs0a353.
M. Nahotko, M. Zych, A. Januszko-Szakiel, and M. Jaskowska, “Big data-driven investigation into the maturity of library research data services (RDS),” The Journal of Academic Librarianship, vol. 49, no. 1, p. 102646, Jan. 2023, doi: 10.1016/j.acalib.2022.102646.
S. R. Shimray, A. Subaveerapandiyan, and N. Ahmad, “Digital transformation in academic libraries: e-resources, OPACs and AI in information discovery,” Reference Services Review, vol. 53, no. 2, pp. 238–255, Oct. 2025, doi: 10.1108/RSR-12-2024-0078.
T. Landivar, R. Rendon, and L. Siguenza-Guzman, “Decision-Making of the University Libraries’ Digital Collection Through the Publication and Citation Patterns Analysis. A Literature Review,” 2022, pp. 80–94. doi: 10.1007/978-3-031-03884-6_6.
P. Roy, “Transforming higher education libraries with data analytics, business intelligence, and business analytics: A review,” Journal of Librarianship and Information Science, vol. 58, no. 1, pp. 23–40, Mar. 2026, doi: 10.1177/09610006241307028.
A. F. Zabidi, “Penerapan Algoritma K-Means untuk Pengelompokan Koleksi Perpustakaan dengan Data Mining,” Media Jurnal Informatika, vol. 16, no. 2, p. 233, Dec. 2024, doi: 10.35194/mji.v16i2.4814.
X. Zhang and J. Zhang, “Analysis and research on library user behavior based on apriori algorithm,” Measurement: Sensors, vol. 27, p. 100802, Jun. 2023, doi: 10.1016/j.measen.2023.100802.
M. Pirniakan, A. Adibifar, F. S. Litooee, M. Tangestanizadeh, and M. Etebar Zadeh, “Enhancing topic modeling in digital libraries through keyword-centric self-supervised learning,” Journal of Librarianship and Information Science, vol. 58, no. 2, pp. 733–749, Jun. 2026, doi: 10.1177/09610006251339838.
M. Marzuki, S. F. Z. Azero, N. A. A. Mohd Zamzuri, and M. R. Abdul Kadir, “A Systematic Literature Review of User Behavior and Personalization in Digital Libraries,” International Journal of Research and Innovation in Social Science, vol. IX, no. I, pp. 4830–4842, 2025, doi: 10.47772/IJRISS.2025.9010372.
H. Salmi Addin, H. Anggraini, H. Nur Riya Putri Yenti, F. Wandan Sari, and I. Hidayat, “Strategi Pengembangan Koleksi Perpustakaan Digital,” Media Informasi, vol. 33, no. 1, pp. 88–95, Jun. 2024, doi: 10.22146/mi.v33i1.11481.
H. Yin, A. Aryani, S. Petrie, A. Nambissan, A. Astudillo, and S. Cao, “A rapid review of clustering algorithms,” Array, vol. 30, p. 100904, Jul. 2026, doi: 10.1016/j.array.2026.100904.
Sherly Rosa Anggraeni, “Implementasi Algoritma Clustering DBSCAN terhadap Pola Navigasi Pengguna di Perpustakaan Digital untuk Mengungkap Zona Buta Akses Informasi dan Optimalisasi Antarmuka Sistem,” SKANIKA: Sistem Komputer dan Teknik Informatika, vol. 8, no. 2, pp. 232–243, Jul. 2025, doi: 10.36080/skanika.v8i2.3524.
R. Sigit, “Penerapan Algoritma K-Means Clustering dalam Menganalisis Pola Peminjaman Buku di Perpustakaan,” The Indonesian Journal of Computer Science, vol. 13, no. 5, Oct. 2024, doi: 10.33022/ijcs.v13i5.4317.
Andre, N. Suciati, H. Fabroyir, and E. Pardede, “Educational Data Mining Clustering Approach: Case Study of Undergraduate Student Thesis Topic,” IEEE Access, vol. 11, pp. 130072–130088, 2023, doi: 10.1109/ACCESS.2023.3332818.
A. Sharma, R. K. Gupta, and A. Tiwari, “Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data,” Math. Probl. Eng., vol. 2016, pp. 1–9, 2016, doi: 10.1155/2016/1564516.
N. P. Sutramiani, I. M. T. Arthana, P. F. Lampung, S. Aurelia, M. Fauzi, and I. W. A. S. Darma, “The Performance Comparison of DBSCAN and K-Means Clustering for MSMEs Grouping based on Asset Value and Turnover,” Journal of Information Systems Engineering and Business Intelligence, vol. 10, no. 1, pp. 13–24, Feb. 2024, doi: 10.20473/jisebi.10.1.13-24.
S. P. Tamba, M. D. Batubara, W. Purba, M. Sihombing, V. M. Mulia Siregar, and J. Banjarnahor, “Book data grouping in libraries using the k-means clustering method,” J. Phys. Conf. Ser., vol. 1230, no. 1, p. 012074, Jul. 2019, doi: 10.1088/1742-6596/1230/1/012074.
T. Xu, “Optimization of Big Data and Intelligent Algorithms in Library Resource Management and Utilization,” in Proceedings of the 2025 4th International Conference on Artificial Intelligence and Education, New York, NY, USA: ACM, Nov. 2025, pp. 200–205. doi: 10.1145/3797552.3797586.
Sherly Rosa Anggraeni, “Implementasi Algoritma Clustering DBSCAN terhadap Pola Navigasi Pengguna di Perpustakaan Digital untuk Mengungkap Zona Buta Akses Informasi dan Optimalisasi Antarmuka Sistem,” SKANIKA: Sistem Komputer dan Teknik Informatika, vol. 8, no. 2, pp. 232–243, Jul. 2025, doi: 10.36080/skanika.v8i2.3524.
S. A. Pratama, “Pengembangan Sistem Rekomendasi Buku Menggunakan Collaborative Filtering,” Jurnal Komputer, vol. 2, no. 2, pp. 81–86, Jun. 2024, doi: 10.70963/jk.v2i2.112.
D. Udariansyah, “Recommender System for Book Review based on Clustering Algorithms,” Journal of Applied Data Sciences, vol. 6, no. 1, pp. 225–235, Jan. 2024, doi: 10.47738/jads.v6i1.492.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Segmentasi Perilaku Pemustaka Menggunakan DBSCAN untuk Optimalisasi Layanan Perpustakaan Digital
Pages: 1225-1236
Copyright (c) 2026 Candra Naya, Ermanto Ermanto, Unggul Prima Dhani
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).
