<![CDATA[In the era of increasingly developing digital information, hoax or fake news is a serious challenge that can influence people's perceptions and decisions. This research aims to develop a hoax news detection method using a machine learning approach on news data. The focus of this research is to identify related hoax news. This research involves collecting news data from leading online news sources and applying machine learning algorithms, including Logistic Regression, Decision Tree, Gradient Boosting, and Random Forest, to classify hoax news. Model performance is measured using accuracy, precision, recall and F1-score metrics, with test results compared with human evaluations that have been trained to recognize hoax news. The results of this research show that the machine learning approach can be successfully used to identify hoax news with a high level of accuracy. However, this study also identified limitations in this approach, such as limited training data and language complexity. Nevertheless, this research makes an important contribution in efforts to overcome the problem of hoax news and provides a basis for further development in hoax news detection using machine learning technology.]]>
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