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

Abstract


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.

Keywords


Makan Bergizi Gratis; Random Forest; Sentiment Analysis; Social Media X; Support Vector Machine

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References


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DOI: https://doi.org/10.18860/cauchy.v11i1.40717

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