Penerapan Long-Short Term Memory dengan Word2Vec Model untuk Mendeteksi Hoax dan Clickbait News pada Berita Online di Indonesia

Soni Marko Nathanniel Tannady, Djoni Haryadi Setiabudi, Alvin Nathaniel Tjondrowiguno


News has become information that is routinely consumed every day and can be accessed easily as technology develops. However, the easy access of readers to news also opens up space for some people to spread clickbait or hoax news to attract readers' attention for personal gain. To overcome this, one of the efforts that can be realized is with a detection model for clickbait and hoax news with machine learning methods. The method used is Long-Short Term Memory. However, with several additional applications such as adding a dropout layer, implementing a callback function and using k-fold cross validation to overcome the problem of overfitting the model which often occurs in related studies. The built model will be tested in a webpage application where users can detect news labels. On the best testing result, testing accuracy for the clickbait detection model are 72.93% and the hoax detection model are 79.17%.


Machine Learning; Natural Language Processing; Long-Short Term Memory; Clickbait; Hoax; Clickbait

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