Sentiment Analysis of Indonesia’s Free Nutritious Meal Program on X Using SVM and Random Forest - Ferdy Aliansyah Hasyim; Talenta Parfaibya Mahenindra; Lilis Sriwahyuni; Alika Azka Shapira; Wigawijayanti Wigawijayanti; Nadhifa Zahra Ghaisani; Mirlan Sujana; Sri Nurdiati; Mohamad Khoirun Najib (CAUCHY: Jurnal Matematika Murni dan Aplikasi, 2026)
The Free Nutritious Meal (Makan Bergizi Gratis/MBG) Program was introduced to address stunting in Indonesia, yet its implementation has sparked diverse public debate. This study aims to map public perception on social media X and compare the performance of Support Vector Machine (SVM) and Random Forest algorithms in sentiment classification. Utilizing a large-scale dataset of 7,452 tweets collected via stratified random sampling from January to October 2025, this research applies TF-IDF feature extraction and SMOTE data balancing. The analysis reveals that positive sentiment dominates at 47.62%, while negative sentiment accounts for 39.8%, and neutral for 12.57%. In model comparison, SVM without SMOTE achieved the best performance with 80.66% accuracy and an F1-Score of 79.79%, outperforming Random Forest, which only reached a maximum accuracy of 72.23% after SMOTE application. These findings provide an objective overview of MBG policy acceptance and methodological insights into the effectiveness of SVM in handling high-dimensional text data.