<![CDATA[This study examines the application of the Naïve Bayes algorithm for sentiment analysis on social media platform X (formerly Twitter) regarding the movie Agak Laen. In the digital era, understanding public opinion is highly important, and this film was chosen as a case study due to the large number of circulating reviews. Naïve Bayes was selected for its efficiency in text classification. The research process began with data collection using the TwCommentExport extension, followed by preprocessing to remove noise such as links and punctuation. The cleaned data were manually labeled into positive or negative sentiments. Subsequently, the data were transformed into numerical representations using TF-IDF feature extraction and trained with the Naïve Bayes algorithm. The dataset was divided into 70% training data and 30% testing data to evaluate the model’s performance. The experimental results demonstrated an accuracy of 75.73%. These findings indicate that Naïve Bayes is an effective method for analyzing movie sentiment, although further improvements in data processing or advanced classification techniques are still possible. This research is expected to provide insights into public responses to the film Agak Laen and serve as a reference for the film industry as well as researchers in understanding audience opinions more comprehensively.]]>
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