Hoax Analyzer for Indonesian News Using Deep Learning Models

Abstract

Fake news has always been in a problem in many parts of the world. Since English is the most dominant language in the world, hoax analyzers are mostly made to cater to news done in English. This study presents various Deep Neural Network (DNN) models: Long Short-Term Memory (LSTM), Bidirectional LSTM (BI-LSTM), Gated Recurrent Unit (GRU), Bidirectional GRU (BI-GRU), and 1-Dimensional Convolutional Neural Network (1D-CNN) as well as two classifiers: Support Vector Machine (SVM) and Naïve Bayes used to predict the validity of news done in Bahasa Indonesia. The results show that DNN models are superior to classifiers in supervised text classification tasks, with 1D-CNN achieving the best result.