Analisis Sentimen Publik terhadap Program Makan Bergizi Gratis Menggunakan Support Vector Machine: Komparasi Teknik Ekstraksi Fitur pada Data Komentar YouTube

Abstract

This study aims to analyze public sentiment toward the Free Nutritious Meal Program (MBG) based on opinions expressed on social media. The research data were collected through a web scraping process using relevant keywords, resulting in a total of 400 comments. After undergoing a series of text preprocessing stages, including case folding, normalization, text cleaning, stopword removal, stemming, and tokenization, 242 comments were retained as the final dataset for analysis. Sentiment labeling was performed automatically using the Indonesia Sentiment Lexicon (InSet), and the comments were categorized into three sentiment classes: positive, negative, and neutral. Sentiment classification was conducted using the Support Vector Machine (SVM) algorithm with three feature extraction techniques, namely Term Presence, Bag of Words (BoW), and Term Frequency–Inverse Document Frequency (TF-IDF). The model performance was evaluated using a confusion matrix and 5-fold cross-validation, combined with the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. The results indicate that neutral sentiment predominated, accounting for 52.89% of the total comments, followed by positive sentiment (31.82%) and negative sentiment (15.29%). These findings suggest that the majority of the public adopted a moderate stance by providing evaluations, recommendations, and expectations regarding the implementation of the Free Nutritious Meal Program (MBG).