Classification of Public Opinion on the Free Nutritional Meal Program on YouTube Media Using the LSTM Method

Berliana Enda Putri, Lisa Diani Amelia, Muhammad Zaky Zaiddan, Luluk Muthoharoh, Ardika Satria +1 more
4/29/2026
cs.CL

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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

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Cite this paper

@article{putri2026classification,
  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},
  year   = {2026},
  eprint = {2604.26312},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2604.26312v1}
}

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