Implementasi Sistem Rekomendasi Film Berbasis Collaborative Filtering: Studi Kasus MovieLens

Fakhrusy Syuyukh(1), Galih Wasis Wicaksono(2*), Ilyas Nuryasin(3),

(1) Universitas Muhammadiyah Malang, Indonesia
(2) Universitas Muhammadiyah Malang, Indonesia
(3) Universitas Muhammadiyah Malang, Indonesia
(*) Corresponding Author

Abstract


Movie recommendation systems often face challenges such as data sparsity and cold-start. This study compares three Collaborative Filtering (CF) approaches: User-Based, Item-Based, and Singular Value Decomposition (SVD) on the MovieLens dataset. Evaluation is performed using RMSE, MAE, Precision@10, and Recall@10 metrics. The results show that SVD with n_factors = 50 provides the best accuracy (RMSE 0.8695; MAE 0.6682), while Item-Based with k = 10 excels in recommendation relevance (Precision@10 0.5510; Recall@10 0.6267). This study provides a reference in selecting a recommendation algorithm based on the context of system needs.

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DOI: http://dx.doi.org/10.30645/jurasik.v10i2.892

DOI (PDF): http://dx.doi.org/10.30645/jurasik.v10i2.892.g866

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