Pengembangan Sistem Rekomendasi Peminjaman Buku dengan Algoritma Pembelajaran Mesin di Perpustakaan Modern - Sukma Kartikasari; Shiefti Dyah Alyusi; Siti Muzaroh (Maktabatuna, 2025)
In the digital era, libraries are required to provide services that adapt to users' evolving needs. As book collections—both digital and physical—continue to grow, machine learning technology offers an effective approach to delivering personalized book recommendations based on users' preferences or borrowing history. This study focuses on developing a book recommendation system utilizing machine learning algorithms to analyze borrowing behavior patterns, genre preferences, and user interaction history. The system is developed using a hybrid filtering approach, which combines collaborative filtering and content-based filtering. The data used includes book metadata (title, category, and author) as well as users’ borrowing records. Evaluation results indicate good system performance, with a precision score of 80%, recall of 72%, and F1-score of 75%. These findings suggest that the hybrid filtering approach can produce more accurate and relevant book recommendations. Consequently, the system can assist users in finding book collections that align with their interests and preferences, thereby improving efficiency and convenience in locating reading materials within the library.