Digital Transformation in Library Recommendation System Using k-NN Collaborative Filtering
Transformasi Digital dalam Sistem Rekomendasi Buku Perpustakaan Menggunakan k-NN
DOI:
https://doi.org/10.33050/tmj.v10i1.2438Abstrak
Perpustakaan, sebagai pusat informasi, memegang peranan penting dalam menyediakan berbagai sumber daya untuk memenuhi kebutuhan informasi pengunjung. Di era digital ini, perpustakaan menghadapi tantangan dalam mengelola koleksi yang sangat besar dan menawarkan layanan personalisasi yang sesuai dengan kebutuhan penggunanya. Tujuan utama dari penelitian ini adalah untuk meningkatkan pengelolaan sumber daya perpustakaan dengan mengembangkan sistem rekomendasi buku yang dipersonalisasi. Sistem ini bertujuan untuk memberikan saran buku yang relevan berdasarkan preferensi individu, khususnya yang disesuaikan dengan kebutuhan akademik dan minat mahasiswa. Untuk mencapai hal ini, penelitian ini menggunakan kombinasi metode User-Based Collaborative Filtering (UBCF) dan k-Nearest Neighbors (k-NN), yang merupakan teknik yang kuat dalam bidang penambangan data. Metode-metode ini digunakan untuk menganalisis kinerja akademik (diukur dengan Indeks Prestasi Semester (IPS) mahasiswa) dan preferensi buku untuk menciptakan sistem rekomendasi yang dipersonalisasi. Penelitian ini menunjukkan bahwa integrasi UBCF dan k-NN secara signifikan meningkatkan akurasi dan relevansi rekomendasi buku, memberikan saran yang lebih sesuai kepada mahasiswa berdasarkan pencapaian akademik dan preferensi mereka. Hasil penelitian menunjukkan bahwa sistem rekomendasi ini tidak hanya meningkatkan pengalaman pengguna, tetapi juga berkontribusi pada peningkatan kinerja akademik mahasiswa dengan menawarkan buku yang sesuai dengan kebutuhan belajar mereka, yang pada akhirnya mendukung tujuan akademik institusi pendidikan tinggi.
Referensi
K. M. K. Chendhur, V. Priya, R. M. Priya, and S. L. Lakshmi, “Book recommender system using improved collaborative filtering,” 4(4), pp. 51–56, 2021.
N. A. Lin, “Personalized book intelligent recommendation system design for university libraries based on ibcf algorithm,” IEEE Access, vol. 12, no. June, pp. 82 015–82 032, 2024.
Y. Tian, B. Zheng, Y. Wang, Y. Zhang, and Q. Wu, “College library personalized recommendation system based on hybrid recommendation algorithm,” 83, March 2023.
S. Xu and Y. Zhang, “Collaborative filtering with implicit feedback for book recommendation,” Journal of Computer Science, vol. 17, no. 2, pp. 89–97, 2021.
R. Suherman and W. Hidayat, “Recommendation system for library using user-based collaborative filtering,” Journal of Information Systems Engineering, vol. 10, no. 2, pp. 45–52, 2022.
H. Hamsinah, U. Rusilowati, and D. Sunarsi, “Analysis of lecturer competency and knowledge in technopreneurship development of student msmes in pts,” Aptisi Transactions on Technopreneurship (ATT), vol. 6, no. 3, pp. 623–638, 2024. [Online]. Available: https://doi.org/10.34306/att.v6i3.365
S. Xu, “Association rule model of on-demand lending recommendation for university library,” Informatica, vol. 44, no. 3, pp. 395–399, 2021.
J. Galang and H. Ramdhan, “Analysis of the acceptance level of e-wallet as a non-cash payment method among indonesian students,” IAIC Transactions on Sustainable Digital Innovation (ITSDI), vol. 5, no. 1, pp. 67–75, 2023.
J. Sch¨opfel, “Smart libraries,” Infrastructures, vol. 7, no. 1, p. 14, 2021.
J. Zhu and T. Li, “A study on the hybrid recommender system based on user preference and contextual factors,” Journal of Information Science, vol. 47, no. 6, pp. 785–798, 2021.
S. S. Amelia et al., “Strategi komunikasi pemasaran sebagai media promosi dalam meningkatkan penjualan pada pt tunas jaya,” Technomedia Journal, vol. 8, no. 3, pp. 331–343, Februari 2024.
N. Rahyuan and F. Maulana, “Personalized book recommendation system using collaborative filtering and content-based filtering,” Journal of Digital Libraries, vol. 20, no. 3, pp. 125–137, 2022.
S. Xu and J. Zhang, “Improved collaborative filtering for book recommendation systems,” Journal of Computational Methods in Sciences and Engineering, vol. 21, no. 4, pp. 843–856, 2021.
J. Perez, G. Lopez, and J. Martin, “Hybrid collaborative filtering for book recommendation in digital libraries,” Library and Information Science Research, vol. 44, no. 3, pp. 215–225, 2022.
P. Baker, H. Johnson, and S. Harris, “User-based collaborative filtering for personalized digital library book recommendations,” Journal of Library and Information Science, vol. 28, no. 4, pp. 213–225, 2022.
Z. Wang, L. Yu, and S. Li, “Personalized book recommendation system using deep learning and collaborative filtering,” Journal of Intelligent Information Systems, vol. 23, no. 3, pp. 123–135, 2022.
R. Gupta and M. Sharma, “Improved item-based collaborative filtering for book recommendation systems,” Advanced Computing and Communication Journal, vol. 14, no. 6, pp. 145–155, 2023.
A. Singh and R. Kumar, “Application of content-based and collaborative filtering for book recommender system,” Journal of Information Science and Technology, vol. 20, no. 1, pp. 45–53, 2021.
J. Siswanto, U. Rahardja, I. Sembiring, K. D. Hartomo, H. D. Purnomo, A. Iriani et al., “Number of road accidents predicting using deep learning-based lstm development models,” in 2023 11th International Conference on Cyber and IT Service Management (CITSM). IEEE, 2023, pp. 1–6.
D. Jannach and A. Felfernig, “Recommender systems: Challenges and opportunities in digital libraries,” Springer, 2021.
L. Johnson and P. Wilson, “Smart libraries: Recommender systems in academic environments,” Digital Library Journal, vol. 19, no. 4, pp. 201–213, 2022.
A. I. Alomran and I. Basha, “An ai-based classification and recommendation system for digital libraries,” Scalable Computing: Practice and Experience, vol. 25, no. 4, pp. 3181–3199, 2024.
H. Li and G. Chen, “Efficient collaborative filtering algorithm for book recommendations in libraries,” Journal of Computational Intelligence, vol. 22, no. 1, pp. 75–85, 2024.
Y. Chen and W. Zhang, “Book recommendation system using deep learning and collaborative filtering,” AI in Libraries, vol. 5, no. 2, pp. 123–135, 2023.
L. Zhao and S. Wang, “Book recommendation using a hybrid method combining content and collaborative filtering,” Library Management Review, vol. 9, no. 3, pp. 231–245, 2021.
I. K. Gunawan, H. T. Sukmana, A. Y. Ardianto et al., “Blockchain technology as a media for sharing information that generates user access rights and incentives,” Blockchain Frontier Technology, vol. 1, no. 01, pp. 44–55, 2021.
F. Liu, Y. Zhang, and X. Li, “Book recommendation based on hybrid collaborative filtering and deep learning,” Journal of Digital Library Technologies, vol. 8, no. 2, pp. 65–77, 2021.
Q. Zhang, R. Li, and J. Wang, “Collaborative filtering with user feedback for book recommendation,” Library Management Journal, vol. 30, no. 1, pp. 49–61, 2021.
X. Sun, Y. Liu, and J. Hu, “Blockchain-based recommendation system for library resources,” Journal of Blockchain and Data Management, vol. 15, no. 2, pp. 145–156, 2023.
A. Rodriguez, F. Silva, and D. Perez, “Recommendation system for digital libraries using collaborative filtering algorithms,” Digital Library Review, vol. 11, no. 4, pp. 245–256, 2021.
I. A. Mutiara, Y. Febriansyah, M. Kamal, R. Z. Ikhsan, and T. Williams, “Revolutionizing logistics business models through big data and blockchain: A business model canvas analysis,” Journal of Computer Science and Technology Application, vol. 1, no. 2, pp. 87–95, 2024.
T. Miller, L. Davis, and S. Lee, “Hybrid collaborative filtering and content-based filtering for book recommendations in academic libraries,” International Journal of Library Science, vol. 19, no. 1, pp. 102–114, 2022.
W. Liu, M. Tang, and J. Zhang, “Improving book recommendation accuracy using k-nn and collaborative filtering algorithms,” Computer Science and Information Technology Journal, vol. 27, no. 3, pp. 88–99, 2024.
M. Davis and Y. Wang, “Deep learning based collaborative filtering for enhanced book recommendation systems,” Artificial Intelligence for Libraries, vol. 3, no. 2, pp. 140–152, 2021.
S. Setiawan, U. Rusilowati, A. Jaya, R. Wang et al., “Transforming human resource practices in the digital age: A study on workforce resilience and innovation,” Journal of Computer Science and Technology Application, vol. 2, no. 1, pp. 84–92, 2025.
L. Zhao, R. Wang, and X. Li, “Personalized book recommendation system using user-based collaborative filtering and content-based techniques,” Library Information Technology Journal, vol. 10, no. 3, pp. 99– 110, 2021.
L. Zhao, W. Zhang, and J. Wang, “Digital transformation in human resource management: Emerging trends and practices,” Journal of Human Resource Technology, vol. 12, no. 3, pp. 45–59, 2021.
S. Lee, J. Kim, and H. Park, “Workforce resilience in the digital age: A study of employee adaptation and innovation,” Journal of Digital Workforce, vol. 8, no. 2, pp. 112–126, 2022.
T. Smith, K. Lee, and R. Johnson, “Hr innovation and digital technologies: A case study on blockchain implementation,” Journal of Business Innovation, vol. 5, no. 4, pp. 200–215, 2023.
U. Rahardja, A. N. Hidayanto, P. O. H. Putra, and M. Hardini, “Immutable ubiquitous digital certificate authentication using blockchain protocol,” Journal of applied research and technology, vol. 19, no. 4, pp. 308–321, 2021.
F. Wang and L. Yang, “Blockchain technology in human resource management: A review of applications and challenges,” Technology and Management Review, vol. 6, no. 2, pp. 67–80, 2024.
R. Johnson and E. Brown, “Digital transformation and hr resilience: Leveraging big data and ai for employee engagement,” Journal of Digital Business, vol. 10, no. 1, pp. 91–104, 2023.
M. Garcia and E. Fernandez, “Human resource practices in the digital era: A focus on workforce adaptation and innovation,” Journal of Organizational Transformation, vol. 11, no. 3, pp. 143–158, 2022.
H. Kim and Y. Choi, “Hr technology and digital innovation: A comparative analysis of digital workforce management tools,” Human Resource Technology Journal, vol. 7, no. 4, pp. 124–135, 2021.
J. Wang and Y. Liu, “Human resource practices and digital innovation: An empirical study on employee performance,” Journal of Human Resource Research, vol. 8, no. 2, pp. 68–80, 2023.
A. Lansonia, M. Austin, and E. A. Beldiq, “Study of student satisfaction in using the moodle e-learning system: Pls-sem approach,” CORISINTA, vol. 1, no. 1, pp. 1–7, 2024.
L. Thompson and M. Walker, “Blockchain and digital transformation in hr practices: A case study of global enterprises,” International Journal of Human Resource Development, vol. 4, no. 1, pp. 55–70, 2021.
H. Liu and X. Zhang, “Digital hr practices and workforce innovation: Exploring new approaches for talent development,” HR Management Review, vol. 5, no. 3, pp. 190–205, 2024.
O. Remadnia, F. Maazouzi, and D. Chefrour, “Hybrid book recommendation system using collaborative filtering and embedding based deep learning,” Informatica, vol. 49, no. 8, 2025.
Kementerian Agama, “1,9 juta buku diakses di perpustakaan digital bimas islam ke- menag,” 2025, diakses: 2025-06-11. [Online]. Available: https://kemenag.go.id/nasional/1-9-juta-buku-diakses-di-perpustakaan-digital-bimas-islam-kemenag-9fH6E
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