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All Journal Jurnal Masyarakat Informatika Advance Sustainable Science, Engineering and Technology (ASSET)
Yeva Fadhilah Ashari
Universitas Diponegoro
Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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An Ensemble-Based Approach for Detecting Clickbait in Indonesian Online Media Sandy Kurniawan; Adhe Setya Pramayoga; Yeva Fadhilah Ashari
Jurnal Masyarakat Informatika Vol 16, No 1 (2025): May 2025
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.1.73115

Abstract

Clickbait headlines are widely used in online media to attract readers through exaggerated or misleading titles, potentially leading to user dissatisfaction and information overload. This study proposes a machine learning approach for detecting clickbait in Indonesian news headlines using classical classification models and ensemble learning. The dataset consists of labeled clickbait and non-clickbait headlines in Bahasa Indonesia, which were processed and represented using TF-IDF vectorization. Three base classifiers, Multinomial Naive Bayes, Logistic Regression, and Support Vector Machine, were integrated using soft voting and stacking ensemble methods. The experimental results indicate that the stacking ensemble model achieved the highest accuracy of 0.7728, while the voting ensemble recorded the best F1-score of 0.7080, outperforming individual classifiers. Despite these gains, the SVM model demonstrated the most substantial decline in accuracy after stopwords removal, dropping by 0.0410. These findings highlight the effectiveness of ensemble learning in enhancing clickbait detection performance and suggest potential for further optimization in model selection and integration strategies.
Development and Evaluation of an IndoBERT-Based NLP Model for Automated Clickbait Detection Sandy Kurniawan; Adhe Setya Pramayoga; Yeva Fadhilah Ashari; Muhammad Afrizal Amrustian
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2637

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.
Co-Authors Adhe Setya Pramayoga Muhammad Afrizal Amrustian Sandy Kurniawan