Analisis Sentimen X Terhadap Isu Industri Sawit Prabowo Subianto Menggunakan TF-IDF dan Machine Learning - Ibrahim Akbar Arga Dewangga; Rama Aria Megantara (Building of Informatics, Technology and Science (BITS), 2026)
This study aims to analyze public sentiment on the X platform regarding the palm oil industry issue associated with Prabowo Subianto and to compare the performance of Decision Tree, Support Vector Machine (SVM), and Random Forest algorithms. The dataset consisted of 3,785 tweets collected through a crawling process. The data were then processed through cleaning, case folding, text normalization, tokenizing, stopword removal, and stemming. Sentiment labeling was conducted using a lexicon-based approach, followed by feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) and traintest data splitting. The labeling results show that public opinion was dominated by positive sentiment with 3,018 tweets (79.7%), while negative sentiment accounted for 767 tweets (20.3%). The experimental results indicate that SVM achieved the best performance with an accuracy of 0.90, followed by Random Forest with 0.86 and Decision Tree with 0.84. SVM also demonstrated more stable performance based on precision, recall, and F1-score across both sentiment classes. These findings indicate that SVM is the most effective model for Indonesian-language sentiment classification on palm oil policy issues and has strong potential to support public policy evaluation based on social media data.