Prediction of Library Book Borrowing Patterns Using The Random Forest Algorithm

Ega Ranaldi Pebriansyah; Susanti; Rahmiati; Triyani Arita Fitri

doi:10.34288/jri.v7i4.409

Authors

Ega Ranaldi Pebriansyah Universitas Sains Dan Teknologi Indonesia
Susanti Universitas Sains Dan Teknologi Indonesia
Rahmiati Universitas Sains Dan Teknologi Indonesia
Triyani Arita Fitri Universitas Sains Dan Teknologi Indonesia

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v7i4.409

Abstract

Libraries play a crucial role in supporting the improvement of public literacy by providing reading materials tailored to users' needs and interests. One of the challenges faced by the Bukit Batu District Public Library is that the collection acquisition analysis process is not yet based on comprehensive borrowing patterns, potentially resulting in inaccurate results. This study aims to predict book borrowing patterns and classify collections into popular and unpopular categories using the Random Forest algorithm. Historical book borrowing data from 2019 to 2024 was used as the primary source in the model training and testing process. Testing was conducted with three data sharing ratios, namely 70:30, 80:20, and 90:10, which resulted in prediction accuracy of 89.19%, 88.69%, and 86.74%, respectively. Based on the analysis results, mathematics books were identified as the most popular collection with 146 borrowings, while social studies books were categorized as unpopular with 122 borrowings. These findings are expected to serve as a reference for libraries in formulating more effective, efficient, and data-based collection management strategies, thereby increasing the relevance and attractiveness of collections for users and supporting the optimization of library services.

Downloads

Download data is not yet available.

References

Ariyono Setiawan, Efendi, A. M. K. T. P., & Wibowo, U. L. N. (2023). Check On-Time Performance of Domestic Airlines Using Random Forest Machine Learning Analysis. Technium Social Sciences Journal, 47, 379–397.

Arya Darmawan, M. B., Dewanta, F., & Astuti, S. (2023). Analisis Perbandingan Algoritma Decision Tree, Random Forest, dan Naïve Bayes untuk Prediksi Banjir di Desa Dayeuhkolot. TELKA - Telekomunikasi Elektronika Komputasi Dan Kontrol, 9(1), 52–61. https://doi.org/10.15575/telka.v9n1.52-61

Daimari, D., Narzary, M., Mazumdar, N., & Nag, A. (2021). Prediksi Ketersediaan Buku Berbasis Machine Learning untuak Sistem Manajemen Perpustakaan. Library Philosophy and Practice, 2021.

Dhewayani, F. N., Amelia, D., Alifah, D. N., Sari, B. N., & Jajuli, M. (2022). Implementasi K-Means Clustering untuk Pengelompokkan Daerah Rawan Bencana Kebakaran Menggunakan Model CRISP-DM. Jurnal Teknologi Dan Informasi, 12(1), 64–77. https://doi.org/10.34010/jati.v12i1.6674

Fadli, A., Limbong, T., Priskila, R., & Handrianus Pranatawijaya, V. (2024). Penggunaan Algoritma Naive Bayes Untuk Memprediksi Kelulusan Mahasiswa. JATI (Jurnal Mahasiswa Teknik Informatika), 8(3), 3773–3779. https://doi.org/10.36040/jati.v8i3.9791

Fatunnisa, A., & Marcos, H. (2024). Prediksi Kelulusan Tepat Waktu Siswa SMK Teknik Komputer Menggunakan Algoritma Random Forest. Jurnal Manajemen Informatika (JAMIKA), 14(1), 101–111. https://doi.org/10.34010/jamika.v14i1.12114

Mahmuda, F., Armys Roma Sitorus, M., Widyastuti, H., & Ely Kurniawan, D. (2023). Clustering Profil Pengunjung Perpustakaan (Studi Kasus Perpustakaan BP Batam). Journal of Applied Informatics and Computing (JAIC), 1(1), 2548–6861. http://jurnal.polibatam.ac.id/index.php/JAIC

Natzir, S. M. (2023). Prediksi Banjir menggunakan Naive Bayes Di Sleman. Jurnal Teknologi Informasi, 14(2), 59–64. https://doi.org/10.52972/hoaq.vol14no1.p59-64

Normawati, D., & Prayogi, S. A. (2021). Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter. J-SAKTI (Jurnal Sains Komputer Dan Informatika), 5(2), 697–711.

Novi Rudiyanti, Mela Aprillia, Fanesha Rahma Fitri, & Pupung Purnamasari. (2025). TEKNIK PENGUMPULAN DATA: OBSERVASI, WAWANCARA DAN KUESIONER. JISOSEPOL: Jurnal Ilmu Sosial Ekonomi Dan Politik, 3(1), 132–138. https://doi.org/10.61787/zk322946

Nurul Chairunnisa. (2023). Prediksi Kemampuan Pembayaran Klien Home Credit Menggunakan Model Random Forest, Decision Tree, Dan Logistic Regression. Jurnal Elektronika Dan Teknik Informatika Terapan ( JENTIK ), 1(3), 140–147. https://doi.org/10.59061/jentik.v1i3.383

Permana, A. A., Taufiq, R., Destriana, R., & Nur’aini, A. (2024). Implementasi Algortima Naïve Bayes Untuk Prediksi Kelulusan Mahasiswa. Jurnal Teknik, 13(1), 65–70. https://jurnal.umt.ac.id/index.php/jt/article/view/10996

Prasojo, B., & Haryatmi, E. (2021). Analisa Prediksi Kelayakan Pemberian Kredit Pinjaman dengan Metode Random Forest. Jurnal Nasional Teknologi Dan Sistem Informasi, 7(2), 79–89. https://doi.org/10.25077/teknosi.v7i2.2021.79-89

Putri, M. (2024). Prediksi Penyakit Stroke Menggunakan Machine Learning Dengan Algoritma Random Forest. Jurnal Infomedia: Teknik Informatika, 9(2), 16–21.

Rahmayanti, A., Rusdiana, L., & Suratno, S. (2022). Perbandingan Metode Algoritma C4.5 Dan Naïve Bayes Untuk Memprediksi Kelulusan Mahasiswa. Walisongo Journal of Information Technology, 4(1), 11–22. https://doi.org/10.21580/wjit.2022.4.1.9654

Rosman, Nining, H. (2022). Peningkatan Kemampuan Otomasi Perpustakaan bagi Pustakawan Madrasah di Provinsi Riau. BIDIK: Jurnal Pengabdian Kepada Masyarakat, 2(2), 51–57. https://doi.org/10.31849/bidik.v2i2.9822

Setiawan, A., Febrio Waleska, R., Adji Purnama, M., Rahmaddeni, & Efrizoni, L. (2024). Komparasialgoritmak-Nearest Neighbor(K-Nn), Support Vector Machine(Svm), Dan Decision Treedalam Klasifikasi Penyakit Stroke. Jurnal Informatika & Rekayasa Elektronika), 7(1), 107–114. http://e-journal.stmiklombok.ac.id/index.php/jireISSN.2620-6900

Sinambela, D. P., Naparin, H., Zulfadhilah, M., & Hidayah, N. (2023). Implementasi Algoritma Decision Tree dan Random Forest dalam Prediksi Perdarahan Pascasalin. Jurnal Informasi Dan Teknologi, 5(3), 58–64. https://doi.org/10.60083/jidt.v5i3.393

Suryati, P. (2022). Analisis Pola Peminjaman Buku Dengan Menggunakan Algoritma Apriori. JIKO (Jurnal Informatika Dan Komputer), 5(1), 17. https://doi.org/10.26798/jiko.v5i1.509

Wibowo, A., & Rohman, A. (2022). Prediksi Predikat Kelulusan Mahasiswa Menggunakan Naive Bayes dan Decision Tree pada Universitas XYZ. 8(200).