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All Journal Journal of Applied Computer Science and Technology (JACOST)
Yesinta, Arlianis Arum
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Computer Science & IT

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Deteksi Clickbait pada Judul Berita Online Berbahasa Indonesia Menggunakan FastText Liebenlito, Muhaza; Yesinta, Arlianis Arum; Musti, Muhamad Irvan Septiar
Journal of Applied Computer Science and Technology Vol 5 No 1 (2024): Juni 2024
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v5i1.655

Abstract

The rise of people accessing news portals has created intense competition between online media to get readers or visitors to maximize their revenue. This is what triggers the development of clickbait. Clickbait can reduce the quality of the news itself, and it also has the potential to be misinformation regarding to news contents as known as fake news. Therefore, it is necessary to detect news titles that contain clickbait. This study aims to obtain an optimal clickbait news title classification model using FastText. To get the optimal model can be done by cleaning the data and optimizing the model's hyperparameters. The model was trained using 9600 training data collected from Indonesian online news. The best model obtained in this study has performance with an accuracy of 77% and an F1-Score of 69%.
Co-Authors Muhaza Liebenlito Musti, Muhamad Irvan Septiar