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All Journal Journal of Islamic Architecture Al-Dzikra: Jurnal Studi Ilmu al-Quran dan al-Hadits JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of System and Computer Engineering ARMATUR : Artikel Teknik Mesin dan Manufaktur Al-Afaq: Jurnal Ilmu Falak dan Astronomi Jurnal Pengabdian kepada Masyarakat Desa (JPMD) Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Mechanical and Manufacture At-Tadzkir: Islamic Education Journal Hikami : Jurnal Ilmu Alquran dan Tafsir
Ahmad Yahya
Department of Islamic Studies Federal College of Education PMB 3045 Kano State, Nigeria
Religion Aerospace Engineering Arts Humanities Astronomy Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Nursing Social Sciences

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Journal : Journal of System and Computer Engineering

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Book Recommendation System Based on Collaborative Filtering: User-Based, Item-Based, and Singular Value Decomposition Analysis Ishak, Ishak; Yahya, Ahmad; Yusri, Yusri
Journal of System and Computer Engineering Vol 6 No 4 (2025): JSCE: October 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i4.2201

Abstract

Recommender systems have become essential in the digital era to help users navigate overwhelming content. This study develops a book recommendation system using three collaborative filtering methods: user-based, item-based, and matrix factorization using singular value decomposition. We evaluate the system on a real-world dataset of 1,149,780 book ratings from 278,858 users across 271,360 books. A subset of 500 active users is used for experimental evaluation. The models are assessed using root mean square error and mean absolute error to measure rating prediction accuracy. The results show that the item-based collaborative filtering method achieves the best accuracy (root mean square error 7.362; mean absolute error 6.761), slightly outperforming the user-based approach (7.365; 6.809) and the matrix factorization method (7.643; 7.413). We analyze the results to understand the performance differences, noting the stability of item similarity as a key factor and the need for optimal tuning in the matrix factorization model. In conclusion, item-based collaborative filtering proved most effective for this context. This work provides insights into the comparative performance of foundational recommendation techniques and highlights practical considerations for improving book recommender systems.
Co-Authors Ahmad, Mustapha Anis Sulalah Arinal Hamni Asep Rachmat, Asep Atmoko Nugroho Devid Frastiawan Amir Sup Dewi Jannati, Eidelweis Fagge, Shehu Dahiru Fathoni, Achmad Fathoni Gusri Akhyar Ibrahim Gustiyawan, Ahmad Handoko, Gesang Dwi Hibatullah, Lutfhi Ilham Hikmah, Hikmatul Karomah Ishak Ishak Islah, Muhammad Amirul Khamid, Mohammad Abdul Koswara, Engkos Maylaffasya, Ghiyatska Shairafi Muhammad, Aliyu Yunus Mushthoza, Zidna Zuhdana Nawawi, Barkaa Alhudaebi Putra, Rafif Mulana Rizky, Al Dihan Fiery Tajudin, Hafidh Whisnumurti Adhiwibowo Winarno, Rivaniansyah Siacahyo Yanuar Burhanuddin Yusri Yusri