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IndoRoBERTa-Base-PDBNews-Growth
ekafitria22/IndoRoBERTa-Base-PDBNews-Growth
IndoRoBERTa-Base-PDBNews-Growth is a machine learning model from ekafitria22. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
The suggested GDP News Indonesian Online classification model on growth GDP yielded an 67.28% F1-score and an accuracy of 85.59% (Epoch=10, Batch Size=32)
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At a glance
- Model type
- roberta
- Access
- Public
- Created
- Apr 12, 2026
- Updated
- Apr 13, 2026
- SHA
- 2056c3bf
Model card
From the Hugging Face model README
Evaluation
The suggested GDP News Indonesian Online classification model on growth GDP yielded an 67.28% F1-score and an accuracy of 85.59% (Epoch=10, Batch Size=32)
Citation
- Plain Text: E. Fitria, A. Afnan and T. F. Abidin, "Detecting GDP Movement and Growth Rates from Indonesian Economic News Using IndoRoBERTa with Neural Sentence Selection," 2026 18th International Conference on Knowledge and Smart Technology (KST), Pattaya, Thailand, 2026, pp. 545-550, doi: 10.1109/KST67832.2026.11432333.
- BibTex: @INPROCEEDINGS{11432333, author={Fitria, Eka and Afnan, Afnan and Abidin, Taufik Fuadi}, booktitle={2026 18th International Conference on Knowledge and Smart Technology (KST)}, title={Detecting GDP Movement and Growth Rates from Indonesian Economic News Using IndoRoBERTa with Neural Sentence Selection}, year={2026}, volume={}, number={}, pages={545-550}, keywords={Economics;Accuracy;Economic indicators;Biological system modeling;Diversity reception;Transformers;Real-time systems;Macroeconomics;Monitoring;Tuning;Gross Domestic Product (GDP);online economic news;text classification;transformer models;IndoRoBERTa;neural sentence selection}, doi={10.1109/KST67832.2026.11432333}}
@INPROCEEDINGS{11432333, author={Fitria, Eka and Afnan, Afnan and Abidin, Taufik Fuadi}, booktitle={2026 18th International Conference on Knowledge and Smart Technology (KST)}, title={Detecting GDP Movement and Growth Rates from Indonesian Economic News Using IndoRoBERTa with Neural Sentence Selection}, year={2026}, volume={}, number={}, pages={545-550}, keywords={Economics;Accuracy;Economic indicators;Biological system modeling;Diversity reception;Transformers;Real-time systems;Macroeconomics;Monitoring;Tuning;Gross Domestic Product (GDP);online economic news;text classification;transformer models;IndoRoBERTa;neural sentence selection}, doi={10.1109/KST67832.2026.11432333}}
Acknowledgment
The authors express their gratitude to all internal and external sources that consistently provide support for our study, especially to Wilson Wongso, Steven Limcorn, Cahya Wirawan, Yogie O. Sihombing, M. J. Mubarok for their openly usable models on HuggingFace.
- Type
- roberta
- Created
- Apr 12, 2026
- Updated
- Apr 13, 2026