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Optimizing BERT Models with Fine-Tuning for Indonesian Twitter Sentiment Analysis

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Comparative evaluation shows fine-tuned BERT model achieves superior sentiment classification accuracy, highlighting usability for Indonesian tweets.

Fine-tuned BERT model achieves accuracy of 91%, outperforming the baseline model.
Experimental results demonstrate improvements in precision, recall, and F1-score with fine-tuning.
Data preprocessing steps included case folding, cleaning, tokenisation, and data augmentation.
Study emphasizes the potential of fine-tuning BERT in low-resource language sentiment analysis.

Afuan et al. (2025) studied this question.

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Comparative evaluation shows fine-tuned BERT model achieves superior sentiment classification accuracy, highlighting usability for Indonesian tweets.