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- Title
Comparative study of predictive models for hoax and disinformation detection in indonesian news.
- Authors
Retno Adiati, Nadia Paramita; Priambodo, Dimas Febriyan; Girinoto; Indarjani, Santi; Rizal, Akhmad; Prayoga, Arga; Beatrix, Yehezikha
- Abstract
Along with the times, false information easily spreads, including in Indonesia. In Press Release No.485/HM/KOMINFO/12/2021, the Ministry of Communication and Information has cut off access to 565,449 negative content and published 1,773 clarifications on hoax and disinformation content. Research has been carried out regarding this matter, but it is necessary to classify fake news into disinformation and hoaxes. This study compares our proposed model, an ensemble of shallow learning predictive models, namely Random Forest, Passive Aggressive Classifier, and Cosine Similarity, and the deep learning model that uses BERT-Indo for classification. Both models are trained using equivalent datasets containing 8757 news, consisting of 3000 valid news, 3000 hoax news, and 2757 disinformation news. This news was obtained from websites such as CNN, Kompas, Detik, Kominfo, Temanggung Mediacenter, Hoaxdb Aceh, Turnback Hoax, and Antara, which were then cleaned from all unnecessary substances, such as punctuation marks, numbers, Unicode, stopwords, and suffixes using the Sastrawi library. At the benchmarking stage, the shallow learning model is evaluated to increase accuracy by applying ensemble learning combined with hard voting. This results in higher values, with an accuracy of 98.125%, precision of 98.2%, F-1 score of 98.1%, and recall of 98.1%, compared to the BERT-Indo model which only achieved 96.918% accuracy, 96.069% precision, 96.937% F-1 score, and 96.882% recall. Based on the accuracy value, the shallow learning model is superior to the deep learning model. This machine-learning model is expected to be used to combat the spread of hoaxes and disinformation in Indonesian news. Additionally, with this research, false news can be classified in more detail, both as hoaxes and disinformation.
- Subjects
ENSEMBLE learning; FAKE news; MACHINE learning; HOAXES; RANDOM forest algorithms
- Publication
International Journal of Advances in Intelligent Informatics, 2024, Vol 10, Issue 3, p504
- ISSN
2442-6571
- Publication type
Academic Journal
- DOI
10.26555/ijain.v10i3.878