<![CDATA[Public perception of government programs is often expressed on social media, making sentiment analysis a vital tool for understanding public opinion. This research presents a comparative study of three machine learning algorithms, Naive Bayes Classifier (NBC), Support Vector Machine (SVM) and Convolutional Neural Network (CNN), applied to sentiment analysis on the Millennial Farmers Program 2024 from Platform X. The study aims to measure the performance of each algorithm in terms of accuracy, execution time, and memory efficiency by adopted a quantitative approach with a comparative experimental design. Sentiment data were collected from platform X (Twitter) between October 2024 and January 2025, with a total of 5,177 raw tweets acquired using the keyword "petani_milenial". After data preprocessing steps, including cleaning, case folding, tokenization, stopword removal, and stemming, the data's sentiment (positive, negative, neutral) was automatically labeled using the IndoBERT model. The sentiment analysis results revealed a dominance of neutral sentiment (832 tweets), followed by negative (331 tweets) and positive (197 tweets). Model performance evaluation showed that SVM achieved the highest overall accuracy at 79%, demonstrating superior capability in classifying neutral sentiment. CNN, with an accuracy of 74.26%, stood out in recall for positive sentiment (73%), making it an effective choice for comprehensive identification of positive sentiment. Meanwhile, NBC, with 78% accuracy, proved to be the most efficient in terms of computation time and memory usage. The study concludes that the optimal model selection greatly depends on the specific use-case priority, whether overall accuracy, positive sentiment identification, or computational efficiency. The results show that CNN outperforms NBC and SVM in accuracy, while NBC is the fastest and consumes the least memory. These findings can help inform future research and implementation in large-scale sentiment analysis applications.]]>
