Abstract
The recommendation system is one method to know the preference consumer by showing the potential object. This recommendation also helps the consumer gets the preference object. One of the popular objects in the recommendation is anime film. In this case, we conduct research to recommend anime films based on ratings of previously watched films. Collaborative filtering is a technique that consists of calculating similarities, predictions, and recommendations. This study is taken from dataset Kaggle which consists of 73,516 users and 12,294 anime. A user’s history will be matched with the whole user’s history with alternating least squares (ALS) method. The anime will be recommended based on that results. This method is expected to help millions of users find the desired anime.
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