Sistem Rekomendasi Film menggunakan User-based Collaborative Filtering dan K-modes Clustering

Ichwanto Hadi, Leo Willyanto Santoso, Alvin Nathaniel Tjondrowiguno


Film is one of the popular entertainment media in the community. The number of titles that have been released makes it difficult for people to find which movies they want to watch. To overcome this problem, it is necessary to have information about the film that will make it easier for the public to find films that fit the user's preferences, therefore the user needs a system that can provide movie recommendations.

The movie recommendation system using User-based Collaborative Filtering is one method that is able to provide recommendations. K-mode Clustering can also be used as an additional accuracy of recommendations by grouping user preferences history.

According to the results of the testing of the k-modes clustering method, the best number of clusters for K-Modes Clustering for film recommendations obtained using the Elbow Method and Silhouette Coefficient is k = 3. From the results of testing the accuracy of the recommendations with Mean Reciprocal Rank (MRR) generated average MRR of 0.17092270381865 for film recommendations with a data train and test ratio of 80%: 20% and an average MRR of 0.15072658511145 for film recommendations with a data train and test ratio of 60%: 40%. From the results of the two tests above, it can be concluded that the level of accuracy of the film recommendations according to the MRR is sufficient because the MRR is close to 0.


Movie Recommendation; K-Modes; User-based Collaborative Filtering; Silhouette Coefficient; MRR

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