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

arXiv cs.CL / 4/30/2026

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Key Points

  • The paper analyzes public sentiment toward Indonesia’s Free Nutritious Meal Program (MBG) by classifying emotions expressed in 7,733 YouTube comments.
  • It applies an LSTM (Long Short-Term Memory) model for sentiment classification and reports an overall accuracy of 89%.
  • Performance is stronger for negative sentiment (F1-score 0.94) than for positive sentiment (F1-score 0.55), largely because negative examples make up 87.7% of the dataset.
  • The study concludes that LSTM is effective for sentiment analysis of Indonesian text on social media, while emphasizing that imbalanced data remains a key challenge.
  • Overall, the research aims to support social-media-based evaluation of public policy through sentiment analytics.

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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