Fine-tuned IndoBERT for stock market sentiment analysis: evidence from CNBC Indonesia news

Tri Agung Jiwandono, MS Hendriyawan Achmad, Suhirman Suhirman

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.

Keywords


Financial news classification; IndoBERT sentiment analysis; Stock market sentiment; Transformer models

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DOI: http://doi.org/10.11591/ijeecs.v42.i3.pp774-785

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES).

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