<![CDATA[Movie recommendation systems have become an interesting research topic, as they can assist users in finding movies that match their preferences. The Content-Based Filtering method is one of the methods commonly used in film recommendation systems, which focuses on film content to make recommendations. This study aims to develop a film recommendation system using the Content-Based Filtering method. This method is used to make film recommendations based on the similarity of the features of the films that have been watched by the user. This research uses case studies at SMK IDN Boarding School, where this film recommendation system was built as a means of learning data mining materials for students. Movie data is taken from the film API and processed using an algorithm to extract features from film content. Then, the system recommends films based on the similarity of features with the films that are liked by the user. The test results show that the developed film recommendation system has good performance in recommending films based on user preferences. Users can provide feedback to improve the accuracy of recommendations and the system can continue to be improved using more sophisticated methods.]]>
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