Implementasi passive aggressive classifier untuk mendeteksi berita fake atau real
Abstract
The proliferation of fake news or hoaxes has become an increasingly pressing issue in today's digital information era. To address this problem, an effective and efficient detection algorithm is necessary. In this study, we propose the implementation of the Passive Aggressive Classifier to identify fake or real news. The Passive Aggressive Classifier is an adaptive machine learning algorithm capable of classifying data online by adjusting to emerging patterns. A dataset consisting of fake and real news is used to train the detection model. The implementation process involves text vectorization using the TF-IDF scheme and model training using the Passive Aggressive Classifier algorithm. The model's performance is evaluated using standard metrics such as accuracy, precision, recall, and F1-score. The research findings demonstrate that the implementation of the Passive Aggressive Classifier is capable of accurately detecting fake news. Consequently, this approach can be employed as an effective solution to swiftly and accurately counteract the spread of fake news
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References
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