A Performance Comparison of Machine Learning Algorithms in Text Analysis of the Free Lunch Program
DOI:
https://doi.org/10.57152/predatecs.v4i1.2093Keywords:
Free Lunch Program, Machine Learning, Natural Language Processing, Public Opinion, Sentiment AnalysisAbstract
The rise of public discourse on social policies in digital platforms necessitates accurate sentiment analysis to support data-driven policymaking. This study investigates the performance of three machine learning algorithms, Naive Bayes, Random Forest, and Support Vector Machine (SVM), in classifying public sentiment toward Indonesia’s Free Lunch Program. Data were collected via web scraping of YouTube comment sections, preprocessed with standard Natural Language Processing (NLP) techniques, and transformed with TF-IDF vectorization. Each algorithm was evaluated using accuracy, precision, recall, and F1-score on a three-class sentiment classification task with classes positive, neutral, and negative. Results show that Random Forest outperformed the other models with an accuracy of 93.55%, followed by SVM at 82.54%, and Naive Bayes at 75.71%. Random Forest demonstrated strong consistency across all sentiment categories, while SVM also delivered competitive results, especially for neutral sentiment. Naive Bayes, although efficient, struggled with imbalanced data, particularly in detecting negative sentiment. These findings highlight the superiority of ensemble models, such as Random Forest, in handling complex sentiment tasks and provide a foundation for developing robust NLP-based public opinion analysis tools for social policy evaluation. Unlike previous studies that primarily focused on product reviews or political sentiment, this research provides a comparative evaluation of machine learning algorithms for analyzing public opinion on Indonesia's Free Lunch Program using YouTube comments. The findings contribute practical guidance for selecting appropriate sentiment classification models to support evidence-based public policy evaluation
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Copyright (c) 2026 Juanda Alra Baye, Erliandika Syahputra, Hilmy Abdurrahim, Abid Aziz Adinda, Muhammad Rakha Athallah, Muhammad Zahid Ramadhan
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