<![CDATA[Clickbait has become a pervasive issue in online news media, particularly in the Indonesian digital information ecosystem, where sensational headlines are frequently used to attract user attention at the expense of content accuracy. This phenomenon not only degrades information quality but also contributes to the spread of misinformation. To address this challenge, this study proposes an ensemble-based machine learning approach for detecting clickbait in Indonesian-language news articles by jointly analyzing headlines and full article content. The proposed method employs Term Frequency–Inverse Document Frequency (TF-IDF) feature extraction with extended n-gram configurations to capture both lexical and contextual patterns characteristic of clickbait. Three baseline classifiers, Multinomial Naïve Bayes, Logistic Regression, and Support Vector Machine are integrated into a hard voting ensemble framework to leverage their complementary strengths. The experiments were conducted on the CLICK-ID dataset, consisting of annotated Indonesian news articles, using an 80:20 train–test split. Experimental results demonstrate that the proposed ensemble model outperforms all individual baseline classifiers, achieving an overall accuracy and F1-score of 93%. The ensemble approach shows notable improvements in recall for the clickbait class, indicating its effectiveness in minimizing false negatives. Furthermore, qualitative analysis using word cloud and bigram visualization reveals distinct linguistic patterns between clickbait and non-clickbait articles, supporting the discriminative capability of the extracted features. These findings confirm that combining TF-IDF with ensemble learning provides a robust and effective solution for clickbait detection in Indonesian online news. The proposed model contributes to the development of more reliable content filtering systems and supports efforts to improve information quality in digital media environments.]]>
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