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Abstrak :
Sistem rekomendasi kini telah menjadi fitur yang umum digunakan pada berbagai situs, termasuk situs katalog buku dan toko buku daring. Adanya sistem rekomendasi pada situs-situs tersebut berperan penting dalam proses pengambilan keputusan pengguna. Dua jenis sistem rekomendasi yang umum digunakan adalah content-based filtering dan collaborative filtering. Penelitian terdahulu menunjukkan bahwa statistical metrics bukan merupakan ukuran yang tepat untuk menentukan kualitas suatu sistem rekomendasi. Salah satu pendekatan lain adalah mengevaluasi sistem rekomendasi berdasarkan persepsi dari pengguna yang menggunakannya. Pada penelitian ini, dilakukan perbandingan antara persepsi pengguna terhadap content-based filtering dengan top-N recommendations dan collaborative filtering dengan matrix factorization menggunakan metode survei kuantitatif yang mengukur accuracy, diversity, novelty, perceived usefulnes, overall satisfaction dan use intention terhadap rekomendasi yang dihasilkan kedua jenis sistem rekomendasi. Hasil penelitian menunjukkan bahwa sistem rekomendasi content-based filtering memiliki accuracy, diversity, perceived usefulness, overall satisfaction dan use intention yang lebih tinggi daripada sistem rekomendasi collaborative filtering. Namun, tidak terdapat perbedaan nilai novelty yang signifikan antara sistem rekomendasi content-based filtering dan collaborative filtering. ......Recommendation system is now a common feature used in various sites, including online book catalogs and bookshops. The existence of recommendation systems on these sites has an important role in users' decision-making processes. Two of the most commonly used types of recommendation systems are content-based filtering and collaborative filtering. Literature has shown that statistical metrics are not suitable to measure the quality of recommendation systems. Instead, a recommendation system can be evaluated based on its users’ perceived qualities. Through this research, a comparison of users’ perception of content-based filtering with top-N recommendations and collaborative filtering with matrix factorization is conducted with a quantitative survey method which evaluates accuracy, diversity, novelty, perceived usefulness, overall satisfaction and use intention of recommendations produced by both recommendation systems. The results suggest that the content-based recommendation system has higher accuracy, diversity, perceived usefulness, overall satisfaction and use intention than collaborative filtering ones. However, there is not any significant difference between the novelty values of content-based and collaborative filtering recommendation systems.