Indonesia's Fake News Detection using Transformer Network

TL;DR

This paper explores fake news detection in Indonesia using various machine learning models, finding that BERT with Transformer Network achieves the highest accuracy of 90% on a dataset of Indonesian news.

Contribution

It introduces a novel application of BERT with Transformer Network for fake news detection in Bahasa Indonesia, filling a gap in existing literature.

Findings

BERT with Transformer Network outperforms other models.

Achieved 90% accuracy in fake news detection.

Dataset from turnbackhoax.id used for training and testing.

Abstract

Fake news is a problem faced by society in this era. It is not rare for fake news to cause provocation and problem for the people. Indonesia, as a country with the 4th largest population, has a problem in dealing with fake news. More than 30% of rural and urban population are deceived by this fake news problem. As we have been studying, there is only few literatures 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. The dataset can be accessed at https://github.com/JibranFawaid/turnbackhoax-dataset. This dataset will be combined with other available datasets. 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%.