Penerapan Algoritma Decision Tree dalam Analisis Sentimen Penerima MBG di Indonesia untuk Menilai Pandangan dan Preferensi Penerima - Maulidia Safitri; Kustiyono (Institut Teknologi Garut, 2026)
The Free Nutritious Meals Program (MBG) is one of the government’s efforts to improve the public’s nutritional status, making it important to analyze public opinion regarding this program on social media. This study aims to classify public sentiment toward the MBG Program using the Decision Tree algorithm. The research data was obtained from Instagram and X (Twitter), comprising 1,016 data points collected through scraping, preprocessing, and sentiment labeling. The research stages included text preprocessing, feature extraction using TF-IDF, and the application of k-fold cross-validation during model training and testing. The evaluation results show that the Decision Tree model achieved an accuracy of 89.07%, with a precision of 85.16% for the positive class and 95.93% for the negative class, as well as a recall of 97.35% for the positive class and 78.67% for the negative class. The classification results show that positive sentiment is slightly more dominant than negative sentiment on both social media platforms. These findings indicate that public opinion on social media tends to respond positively to the MBG Program, although the model still has limitations in recognizing negative sentiment in a balanced manner. This study also has limitations because the data comes from only two social media platforms, and the sentiment labeling process still has the potential to contain bias despite manual validation.