Public Sentiment Analysis of the Free Meal Program: A Comparison of Naive Bayes and Support Vector Machine Methods on the Twitter (X) Social Media Platform

Saleh, Muhammad Farhan
Imanda, Rahmi

JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Vol. 9 No. 1 (2025): February 2025

The problems of nutrition, including stunting, remain a challenge in Indonesia. Therefore, Prabowo and Gibran launched the 2024 Free Meal Program, which provides free lunch to every school child as well as pregnant mothers. This research analyzes public sentiment towards this program using data from X with Naïve Bayes and Support Vector Machine (SVM) methods. The data was analyzed through crawling, preprocessing, labeling, and feature extraction using TF-IDF. The results showed a predominance of positive sentiment towards the program, with SVM performing better in sentiment classification, achieving 86.42% accuracy compared to Naïve Bayes with 67.9%. The findings can guide policymakers in improving the communication strategy and implementation of the Free Meal Program to make it more impactful for Indonesians.