Classification of Public Opinion on the Free Nutritional Meal Program on YouTube Media Using the LSTM Method
Abstract
Public opinion towards the Free Nutritious Meal Program (MBG) on YouTube social media reflects diverse community responses. This study applies the Long Short-Term Memory (LSTM) method to classify sentiments from 7,733 YouTube comments. The results show that the LSTM model achieves 89% accuracy, with strong performance on negative sentiment (F1-score 0.94) but weaker performance on positive sentiment (F1-score 0.55) due to class imbalance, as negative data account for 87.7% of the dataset. These findings confirm the effectiveness of LSTM for sentiment analysis of Indonesian text while highlighting the challenge of imbalanced data. This research contributes to social media-based public policy evaluation
Cite
@article{arxiv.2604.26312,
title = {Classification of Public Opinion on the Free Nutritional Meal Program on YouTube Media Using the LSTM Method},
author = {Berliana Enda Putri and Lisa Diani Amelia and Muhammad Zaky Zaiddan and Luluk Muthoharoh and Ardika Satria and Martin Clinton Tosima Manullang},
journal= {arXiv preprint arXiv:2604.26312},
year = {2026}
}
Comments
10 pages 3 figures 3 tables Conference submission on YouTube sentiment classification using LSTM for the Free Nutritious Meal Program