<![CDATA[The development of digital streaming platforms has led to information overload, making it difficult for users to choose a movie. This study aims to design and implement a movie recommendation system to address this issue. The method used is Content-Based Filtering (CBF), which focuses on textual content analysis. This system uses the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to weight words in movie synopses, and Cosine Similarity to calculate similarities between movies. The results of the study indicate that the system was successfully implemented. Functional tests showed that the system was able to provide highly relevant recommendations when the synopsis keywords were unique, such as in the 'Batman' test. However, the system also showed limitations when the keywords were ambiguous, such as in the 'Hulk' test which incorrectly matched the name "Bruce". For quantitative accuracy evaluation, the system was tested using the Precision@k metric and achieved an average precision value of 30.00% at P@5. The conclusion of this study is that the synopsis-based CBF method was successfully implemented, but its performance was shown to be highly dependent on the quality and uniqueness of the keywords in the synopsis data]]>
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