TY - GEN
T1 - Indonesia's Fake News Detection using Transformer Network
AU - Fawaid, Jibran
AU - Awalina, Aisyah
AU - Krisnabayu, Rifky Yunus
AU - Yudistira, Novanto
N1 - Publisher Copyright:
© 2021 ACM.
PY - 2021/9/13
Y1 - 2021/9/13
N2 - 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%.
AB - 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%.
KW - BERT
KW - fake news
KW - Transformer Network
UR - https://www.scopus.com/pages/publications/85118840888
U2 - 10.1145/3479645.3479666
DO - 10.1145/3479645.3479666
M3 - Conference contribution
AN - SCOPUS:85118840888
T3 - ACM International Conference Proceeding Series
SP - 247
EP - 251
BT - Proceedings of 2021 International Conference on Sustainable Information Engineering and Technology, SIET 2021
PB - Association for Computing Machinery
T2 - 6th International Conference on Sustainable Information Engineering and Technology, SIET 2021
Y2 - 13 September 2021 through 14 September 2021
ER -