<![CDATA[This study aims to compare the performance of Support Vector Machine (SVM) and Random Forest (RF) in classifying public sentiment toward environmental issues on social media X (formerly Twitter) and to develop a web-based system for sentiment monitoring and visualization. A total of 47,245 tweets from 2021–2025 were collected using 24 environmental keywords. The data were processed through text cleaning, tokenization, stopword removal, and stemming. Sentiment labeling was performed automatically using a lexicon-based approach with the InSet dictionary, resulting in positive, negative, and neutral classes. After filtering, 13,063 tweets were used for model training. Classification employed TF-IDF features and 5-fold cross-validation. The results indicate that SVM outperformed RF with an accuracy of 83%, compared to 81%. Both models performed well in identifying sentiment polarity, although challenges remain in classifying neutral sentiment. The novelty of this study lies in integrating lexicon-based labeling with machine learning and implementing it in a web-based system for automated analysis and visualization. Practically, this system supports stakeholders in monitoring public opinion and enables data-driven decision-making in environmental policy and management.]]>
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