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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Tri Agung Jiwandono
University of Technology Yogyakarta

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Fine-tuned IndoBERT for stock market sentiment analysis: evidence from CNBC Indonesia news Tri Agung Jiwandono; MS Hendriyawan Achmad; Suhirman Suhirman
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i3.pp774-785

Abstract

Financial sentiment analysis in Indonesian markets faces significant accuracy challenges, with existing models achieving only 78-81% accuracy. We present a fine-tuned IndoBERT-Large model for classifying sentiment in Indonesian stock market news headlines, trained on 9,819 CNBC Indonesia headlines (January 2024-March 2025). Through systematic hyperparameter optimization and stratified vocabulary-balanced splitting, our model achieved 94.20% accuracy, surpassing previous baselines by 4-16 percentage points. These results demonstrate IndoBERT's effectiveness for Indonesian financial NLP and its potential for real-time market monitoring and investment decision support systems.
Co-Authors M.S. Hendriyawan Achmad Suhirman Suhirman