Penerapan Collaborative Filtering untuk Sistem Rekomendasi Film
DOI:
https://doi.org/10.30606/rjti.v4i1.3248Keywords:
Collaborative Filtering, Rekomendasi Film, User-Based, Item-Based, Metrik EvaluasiAbstract
Sistem rekomendasi berperan penting dalam membantu pengguna menemukan konten yang relevan di tengah banyaknya informasi yang tersedia. Penelitian ini mengimplementasikan dan mengevaluasi metode User-Based dan Item-Based Collaborative Filtering untuk sistem rekomendasi film menggunakan dataset MovieLens 100K. Evaluasi dilakukan menggunakan RMSE, MAE, Precision, Recall, dan F1-Score untuk mengukur akurasi prediksi dan relevansi rekomendasi. Hasil penelitian menunjukkan bahwa metode Item-Based Collaborative Filtering memiliki performa lebih baik dibandingkan User-Based Collaborative Filtering dalam hal akurasi prediksi dan relevansi rekomendasi. Keunggulan ini disebabkan oleh stabilitas hubungan antar item dibandingkan preferensi pengguna yang lebih dinamis. Meskipun efektif, metode ini masih menghadapi tantangan seperti sparsity dan keterbatasan jumlah rating pada beberapa film. Penelitian selanjutnya dapat mengeksplorasi pendekatan hibrida yang menggabungkan Collaborative Filtering dengan deep learning atau content-based filtering untuk meningkatkan kualitas
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