📅 22 January 2026
DOI: 10.26877/asset.v8i1.2637
Development and Evaluation of an IndoBERT-Based NLP Model for Automated Clickbait Detection
Advance Sustainable Science, Engineering and Technology (ASSET)
Universitas Persatuan Guru Republik Indonesia Semarang
📄 Abstract
The rapid growth of digital news platforms necessitates reliable and automated systems for maintaining content quality at scale. This study presents the engineering and evaluation of an IndoBERT-based Natural Language Processing (NLP) framework for automated clickbait detection in Indonesian news headlines. The proposed framework is designed as an end-to-end text classification pipeline, incorporating data preprocessing, tokenization, fine-tuning of a pretrained IndoBERT model, and systematic performance evaluation. Experiments were conducted using the CLICK-ID dataset comprising 15,000 Indonesian news headlines, with an 80:20 stratified train–test split. The fine-tuned model achieved an accuracy of 0.83, with a precision of 0.82, recall of 0.77, and an F1-score of 0.79 for the clickbait class. Further evaluation using threshold-independent metrics yielded a ROC-AUC value of 0.89 and an average precision of 0.88, indicating strong discriminative capability under moderate class imbalance. Comparative analysis shows that the proposed approach outperforms prior CNN, Bi-LSTM, and ensemble-based methods evaluated on the same dataset. These results demonstrate that IndoBERT provides a robust foundation for engineering automated clickbait detection systems tailored to Indonesian-language news streams.
🔖 Keywords
#IndoBERT; NLP system design; clickbait detection; machine learning pipeline; model evaluation
ℹ️ Informasi Publikasi
Tanggal Publikasi
22 January 2026
Volume / Nomor / Tahun
Volume 8, Nomor 1, Tahun 2026
📝 HOW TO CITE
Kurniawan, Sandy; Pramayoga, Adhe Setya; Ashari , Yeva Fadhilah; Muhammad Afrizal Amrustian, "Development and Evaluation of an IndoBERT-Based NLP Model for Automated Clickbait Detection," Advance Sustainable Science, Engineering and Technology (ASSET), vol. 8, no. 1, Jan. 2026.
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