PENGEMBANGAN SISTEM REKOMENDASI BUKU BERBASIS KECERDASAN BUATAN UNTUK PERPUSTAKAAN DIGITAL
DOI:
https://doi.org/10.54314/jssr.v8i4.4675Abstract
Abstract: This research aims to develop an artificial intelligence-based book recommendation system to improve the user experience in finding relevant literature in digital libraries. Two main approaches are used: Content-Based Filtering (CBF) and Collaborative Filtering (CF), each of which relies on similar content characteristics and user preference patterns. Research data was collected from book metadata and user interactions at the West Sumatra Regional Library. Test results show that a hybrid approach of CBF and CF can improve recommendation accuracy by up to 92%, compared to using either method alone. This research contributes to the development of modern library information systems that are adaptive to user needs.
Keywords: recommendation system, artificial intelligence, digital library, content-based filtering, collaborative filtering.
Abstrak: Penelitian ini bertujuan mengembangkan sistem rekomendasi buku berbasis kecerdasan buatan untuk meningkatkan pengalaman pengguna dalam menemukan literatur yang relevan di perpustakaan digital. Dua pendekatan utama yang digunakan adalah Content-Based Filtering (CBF) dan Collaborative Filtering (CF), yang masing-masing mengandalkan kesamaan karakteristik konten dan pola preferensi pengguna. Data penelitian dikumpulkan dari metadata buku serta interaksi pengguna di Perpustakaan Daerah Sumatera Barat. Hasil pengujian menunjukkan bahwa pendekatan hybrid antara CBF dan CF mampu meningkatkan akurasi rekomendasi hingga 92%, dibandingkan penerapan metode tunggal. Penelitian ini berkontribusi pada pengembangan sistem informasi perpustakaan modern yang adaptif terhadap kebutuhan pengguna.
Kata kunci: sistem rekomendasi, kecerdasan buatan, perpustakaan digital, content-based filtering, collaborative filtering.
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