A Analisis Sentimen Publik terhadap Program Makan Bergizi Pemerintah (MBG) pada Media Sosial X Menggunakan Metode Support Vector Machine - Windi Haria Ningsi; Ucta Pradema Sanjaya (Jurnal Algoritma, 2026)
The Nutritious Meal Program (MBG) is one of the government’s initiatives to improve the nutritional quality of society. This study aims to analyze public sentiment toward the MBG program on social media platform X in order to provide a quantitative overview of public perception. The data used consisted of 1,938 public posts containing keywords related to the MBG program. The analysis stages included text preprocessing, namely data cleaning, tokenization, stopword removal, and stemming. Furthermore, feature representation was carried out using the TF-IDF method, while sentiment classification was performed using the Support Vector Machine (SVM) algorithm to categorize the data into positive, neutral, and negative sentiments. The results indicate that the classification model achieved an accuracy rate of 96.69 percent, demonstrating excellent model performance. Based on the classification results, sentiment distribution was dominated by negative sentiment, followed by neutral and positive sentiments, indicating that public responses toward the MBG program tend to be critical. These findings suggest that although the MBG program has received significant public attention, there are still various criticisms and feedback that can serve as evaluation material for the government in improving the effectiveness and implementation of the program.