Implementasi Multilayer Perceptron untuk Klasifikasi Berita Hoax dalam Media Sosial - Amanda, Hervilla; Faiza, Nayla; Sofinah Harahap, Lailan (Journal of Multidisciplinary Inquiry in Science, Technology and Educational Research, 2025)
The very fast dissemination of information via social media in the current digital era has facilitated the spread of fake news or hoaxes. Hoax news is false information, often created deliberately to spread or manipulate public opinion. The spread of hoaxes on social media can have serious impacts, such as public unrest. Therefore, automatic detection of hoax news is very important to maintain the integrity of information circulating in society. This research aims to implement the Multilayer Perceptron (MLP) algorithm in classifying news as "hoax" or "not hoax". The MLP algorithm works by learning from training data containing labeled news text. Based on certain patterns and features, this model is expected to be able to detect whether a piece of news is a hoax or not. The implementation of Perceptron for hoax news classification aims to provide a system that can help social media users filter information, so that it can support a healthier and more trustworthy social media ecosystem. This research uses data collection methods from various social media and news sites, data preprocessing, MLP model formation, system implementation, and model evaluation. The implementation results show that the MLP model is able to classify hoax news with an accuracy of 63.1%. It is hoped that these findings can contribute to the development of accurate and efficient hoax detection technology.