Implementation of Deep Learning to Detect Indonesian Hoax News with Convolutional Neural Network Method

Cheevin Yoviananda; Tresna Maulana Fahrudin

doi:10.29138/ijeeit.v4i2.1525

Cheevin Yoviananda Narotama University
Tresna Maulana Fahrudin

DOI: https://doi.org/10.29138/ijeeit.v4i2.1525

Keywords: Convolutional Neural Network, Corpus, Embeddings, Word2Vec

Abstract

This study aims to establish and test a model that is used to determine valid news and hoax news. The method used is the Convolutional Neural Network (CNN) method and Word2Vec as embeddings. The research stages consist of data collection, pre-processing, word embeddings, model formation and testing the results obtained. The data used is 958 news. After testing with the distribution of data by 80% as training data and 20% as test data and 5 times epoch, the model that has been formed can determine valid news and hoax news well. In this study, a model with a vector dimension of 400 as input data and a multiple filter size of 3,4,5 became the best model. The resulting accuracy, precision and recall are 0.91. These results are influenced by the selection of the size of the vector dimensions on the output of Word2Vec, the selection of the filter size on the convolution layer and the addition of the Indonesian Wikipedia corpus into the corpus used.

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