Public Response on X to the Revocation of Indonesia’s 3-Kg LPG Retail Ban: A Support Vector Machine Study - Ni Nyoman Asti Sri Wahyuni; I Gede Iwan Sudipa; Ni Nyoman Ayu J. Sastaparamitha; Ayu Gede Willdahlia; I Gusti Ayu Agung Mas Aristamy (Indonesian Journal of Data and Science, 2025)
This study examines public responses on X to the 3-Kg LPG retail ban implemented on February 1, 2025, and revoked on February 4, 2025, which caused widespread shortages, long queues, and limited access, particularly for citizens living far from official distribution points. A total of 2,524 Indonesian-language tweets were collected via crawling and systematically processed through text cleaning, tokenization, normalization, stopwords removal, and stemming, followed by automatic labeling using the Indonesian Sentiment (InSet) Lexicon. After removing 229 neutral tweets, 1,405 tweets (61.2%) were classified as negative and 890 tweets (38.8%) as positive, with the study focusing on these two sentiment classes. Text features were extracted using TF-IDF, and classification was conducted using a linear-kernel Support Vector Machine (C = 0.1) with an 80:20 train-test split. The model achieved an overall accuracy of 84%, with precision, recall, and F1-score of 0.82, 0.94, and 0.88 for the negative class, and 0.87, 0.68, and 0.76 for the positive class. Results indicate that negative sentiment was dominated by criticism related to LPG shortages and insufficient policy communication, while positive sentiment reflected user relief over restored supply and hopes for fairer distribution in the future. These findings suggest that revoking the ban did not fully restore public perception, highlighting the necessity for more effective policy dissemination and stricter monitoring of 3-Kg LPG distribution. The study also emphasizes the importance of leveraging social media, particularly X, as a real-time source for monitoring public opinion and evaluating the effectiveness of energy distribution policies in Indonesia.