ANALISIS SENTIMEN SOSIAL MEDIA X TERHADAP PROGRAM MAKAN BERGIZI GRATIS MENGGUNAKAN MACHINE LEARNING
DOI:
https://doi.org/10.47111/jti.v20i2.24652Keywords:
Sentiment Analysis, Social Media X, Machine Learning, IndoBERT, Support Vector Machine.Abstract
The rapid development of digital technology has made social media an important source of information and a platform for expressing public opinion on government policies, including the Free Nutritious Meal Program which aims to improve public health, quality of life, and reduce nutritional and economic problems; this study analyzes public sentiment toward the program based on 10,381 comments from users of social media platform X (Twitter) collected through web scraping using Google Colab, which were then processed through cleaning, normalization, tokenization, stopword removal, and stemming, and automatically labeled into positive, negative, and neutral categories using IndoBERT, before being classified using four machine learning algorithms, namely Naive Bayes Classifier, Support Vector Machine, K-Nearest Neighbor, and Random Forest, with the results showing that SVM achieved the highest accuracy of 74%, followed by Random Forest at 70%, Naive Bayes at 69%, and KNN at 43%, while neutral and positive sentiments were dominant, indicating that the program has received a generally favorable response from the public, and therefore the findings are expected to serve as evaluation material for the government to improve the effectiveness and implementation of the Free Nutritious Meal Program in the future.
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