<![CDATA[The development of public opinion regarding the revision of the 2025 Indonesian National Armed Forces Law (UU TNI) on social media has generated various responses that are difficult to analyze manually due to the large and unstructured amount of data. This condition requires a computational approach that is able to systematically identify public sentiment trends. This study aims to analyze public sentiment towards the revision of the 2025 TNI Law using the TF-IDF-based Naïve Bayes algorithm and evaluate the performance of the classification model used. The research data was obtained through crawling techniques from YouTube user comments related to the revision of the 2025 TNI Law. The data processing stages include cleaning, case folding, tokenizing, normalization, stopword removal, and stemming before TF-IDF weighting and the classification process using Naïve Bayes. The results of the study of 1826 data show that public sentiment is dominated by the neutral category at 79.8%, while positive sentiment is 13.1% and negative sentiment is 7.0%. The model evaluation yielded an accuracy of 77.11%, but the model showed a bias toward the majority class, resulting in suboptimal classification of positive and negative sentiments. Based on these results, the Naïve Bayes method is quite effective as an initial approach in sentiment analysis, but it still has limitations in handling imbalanced datasets and the complex characteristics of social media language. Therefore, the development of more adaptive methods is needed to improve the quality of sentiment classification results.]]>
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