Jibran Fawaid, Aisyah Awalina, Rifky Yunus Krisnabayu, Novanto Yudistira
Fake news is a problem faced by society in this era. It is not rare for fake news to cause provocation and problems for the people. Indonesia, as a country with the 4th largest population, has a problem in dealing with fake news. More than 30% of the rural and urban population are deceived by this fake news problem. As we have been studying, there is only a little literature on preventing the spread of fake news in Bahasa Indonesia. So, this research is conducted to prevent these problems. The dataset used in this research was obtained from a news portal that identifies fake news, turnbackhoax.id. Using Web Scrapping on this page, we got 1116 data consisting of valid news and fake news. This dataset will be combined with other available datasets. The dataset is then processed by eliminating irrelevant words and dividing the data into training and testing data with a ratio of 80:20. All neural network methods use word embedding with Word2Vec with 50 dimensions. The methods used are CNN, BiLSTM, Hybrid CNN-BiLSTM, and BERT with Transformer Network. This research shows that the BERT method with Transformer Network has the best results with an accuracy of up to 90%. © 2021 ACM.
Brawijaya University, Indonesia; Universitas Brawijaya, Indonesia