An Ensemble-Based Approach for Detecting Clickbait in Indonesian Online Media

Abstrak

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.