<![CDATA[The high volume of public comments on YouTube regarding the DPR Demonstrasion August 2025, which reached 43,910 raw data, presents a significant challenge in conducting efficient sentiment analysis. Time and cost limitations in manual labeling for large-scale datasets are a major obstacle in the development of predictive models. This study aims to address this problem by proposing a hybrid approach that integrates Lexicon-Based auto-labeling with a comparative evaluation of five Machine Learning algorithms. The research methodology included a text preprocessing stage that generated 40,097 unique comments, feature extraction using TF-IDF, and data sharing with an 80:20 ratio. The performance of the Support Vector Machine algorithm was comprehensively compared to Random Forest, Decision Tree, K-Nearest Neighbors, and Naive Bayes. The results of the experiment showed that the SVM model recorded the most superior performance with an accuracy of 96.5% and a weighted F1-Score of 0.966. This score significantly outperformed other benchmarking algorithms, where Random Forest came in second place with 89.2% accuracy, followed by Decision Tree at 85.6%, KNN at 84.6%, and Naive Bayes at the lowest with 84.0%. These findings validate that the integration of Lexicon-Based labeling with SVM classification is a highly accurate, robust, and efficient solution for handling sentiment analysis on large-scale social media data in Indonesia.]]>
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