<![CDATA[The rise of digital technology encourages the public to actively voice their opinions through social media, including in response to political issues such as the policy on increasing the remuneration of the Indonesian House of Representatives (DPR RI). This research aims to analyze public sentiment towards this issue on the YouTube platform using a comparative approach with three Machine Learning algorithms: Naïve Bayes, Support Vector Machine, and Random Forest. The data was acquired from viewer comments via the YouTube Data Application Programming Interface (API), totaling 78,866 lines of comments collected from seven videos discussing the DPR RI controversy. The data collection process utilized the googleapiclient.discovery.build module with API version V3, where the API_Key served as the authentication key to access data from YouTube. The research stages included preprocessing for data cleaning, sentiment labeling based on the InSet Lexicon Based method, and the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance in the data. The results show that before SMOTE application, the Support Vector Machine (SVM) model achieved the highest accuracy of 89%, followed by Random Forest at 81%, and Naïve Bayes at 62%. After applying SMOTE, the performance of all three models increased significantly, with SVM obtaining the highest accuracy of 93%, followed by Random Forest at 86%, and Naïve Bayes at 75%. For the positive class, SVM also demonstrated the best performance with a Precision value of 96%, Recall of 95%, and an F1-Score of 95%. Overall, the findings of this study confirm that SVM is superior in maintaining class balance in classification, both before and after SMOTE. The Machine Learning-based sentiment analysis approach is proven capable of providing a comprehensive overview of public opinion on political issues, while also offering important input for policymakers in formulating more transparent and responsive communication strategies.]]>
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