Fake news classification for Indonesian news using Extreme Gradient Boosting (XGBoost)

Fake news or commonly known as a hoax has become one of the most visible cybercrime. Hoax news dissemination harms the social community, such as raising hatred towards something both individuals and groups. This paper is to classify amongst hoaxes and valid news utilizing Extreme Gradient Boosting (...

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Bibliographic Details
Published in:IOP conference series. Materials Science and Engineering 2021-03, Vol.1098 (5), p.52081
Main Authors: Haumahu, J P, Permana, S D H, Yaddarabullah, Y
Format: Article
Language:English
Subjects:
Crime
Hoaxes
Machine learning
Model accuracy
News
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Summary:Fake news or commonly known as a hoax has become one of the most visible cybercrime. Hoax news dissemination harms the social community, such as raising hatred towards something both individuals and groups. This paper is to classify amongst hoaxes and valid news utilizing Extreme Gradient Boosting (XGBoost) method in this research based on Indonesian news. The dataset used is Indonesian news about Indonesia itself and the world from 2015 to early 2020. The study used 500 news data including 250 valid news and 250 hoax news, divided into 80% training data and 20% test data. The result of this study shows that the machine learning model created using XGBoost has an accuracy value of 89%, with the precision value of 90% and recall value 80%.
ISSN:1757-8981
1757-899X
DOI:10.1088/1757-899X/1098/5/052081