Deteksi Berita Palsu Menggunakan Metode Random Forest dan Logistic Regression
DOI:
https://doi.org/10.30865/jurikom.v9i2.3979Keywords:
News, Detection, Random Forest, Supervised LearningAbstract
Fake news is information that is presented incorrectly or falsely. Of course, if the spread of fake news continues, it can result in wrong knowledge of the information obtained. One of the efforts to prevent the spread of fake news is by detecting whether the news is genuine or fake in order to provide an explanation to the readers of the related news. This study aims to detect fake news using a supervised learning random forest model. The news dataset used contains 6256 rows of titles that have a fake or real class. The dataset first goes through a cleaning, tokenization, and stemming process to break sentences into words. The results obtained using the random forest model of 84%, this result is higher than using the logistic regression model of 77%.
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