Literature Review: Comparison of Machine Learning Algorithms for Sentiment Analysis of Free Nutritious Meals

Abstract

The Free Nutritious Meal (FNM) program has triggered massive public responses on social media, driving numerous machine learning–based sentiment analysis studies. However, there has been no comprehensive review comparing the effectiveness of these methods. This study adopts a Systematic Literature Review (SLR) approach on 18 studies (2024–2026) to evaluate the performance of computational algorithms and map trends in public sentiment. The main contribution of this research is to provide an empirical guide for selecting Indonesian-language text classification models, while also offering insights into shifts in public perception. Key findings indicate that Support Vector Machine (SVM) is the most frequently used method, whereas the highest accuracy (97%) was achieved by a combination of Logistic Regression, SVM, and Random Forest on large datasets. Temporally, sentiment trends shifted from budget skepticism (2024) to positive acceptance during program implementation (2025–2026). The study’s implications support policymakers in evaluating program effectiveness in real time. The scope and limitations of this research focus on literature within a specific timeframe, with performance evaluation emphasizing quantitative accuracy metrics.