Abstrak

The transition toward sustainable energy has become a strategic priority for Indonesia, particularly through the implementation of bioethanol-blended fuel policies. However, public perception toward this policy remains diverse and dynamic, especially as expressed on social media platforms. This study aims to analyze public sentiment regarding the implementation of bioethanol-blended fuel (E10) policies on X (Twitter) and to compare the performance of traditional machine learning and Transformer-based models in sentiment classification.

This research adopts a quantitative experimental approach using Natural Language Processing (NLP) techniques. A total of 2,501 tweets were collected through web crawling and processed using a Dual Pipeline Preprocessing approach. Sentiment labeling was conducted using the VADER method with manual validation. Two classification models were implemented, namely Support Vector Machine (SVM) as the baseline model and IndoBERT as the Transformer-based model. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics.

The results indicate that the IndoBERT model outperforms SVM, achieving an accuracy of 81.64% and an F1-score of 81.39%, compared to SVM with an accuracy of 72.46% and an F1-score of 72.02%. The performance improvement of 9.18% demonstrates the superiority of Transformer-based models in capturing contextual semantics in unstructured social media text. In addition, sentiment analysis results reveal that public opinion is predominantly positive toward the policy, although concerns regarding technical and economic aspects remain.

This study contributes by providing empirical insights into public perception of energy policy and demonstrating the effectiveness of Transformer-based models for sentiment analysis in the Indonesian language context