Fake News Classification for Indonesian News Using Extreme Gradient Boosting (XGBoost) John Pierre Haumahu (a*), Silvester Dian Handy Permana (a), Yaddarabullah (a)
a) Program Studi Teknik Informatika, Universitas Trilogi
Jl. TMP Kalibata No.1, Jakarta 12760, Indonesia
*johnhaumahu[at]trilogi.ac.id
Abstract
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%.
