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All Journal Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Andrianarisoa, Sitraka Herinambinina
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Computer Science & IT Engineering Mechanical Engineering Transportation

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A Deep Learning Approach to Fake News Classification Using LSTM Andrianarisoa, Sitraka Herinambinina; Ravelonjara, Henri Michaël; Suddul, Geerish; Foogooa, Ravi; Armoogum, Sandhya; Sookarah, Doorgesh
Vokasi Unesa Bulletin of Engineering, Technology and Applied Science Vol. 2 No. 3 (2025)
Publisher : Universitas Negeri Surabaya or The State University of Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/vubeta.v2i3.39360

Abstract

The rapid spread of misinformation on digital platforms poses a major challenge today. The ability to detect false information is essential to mitigate the associated harmful consequences. This research presents a deep learning approach for detecting fake news using Long Short-Term Memory (LSTM) model, which captures linguistic patterns and long-term dependencies in text. Our approach consists of optimizing the model through different experiments based on hyperparameter tuning, on a pre-processed dataset. The evaluation is performed using different metrics such as accuracy, precision, recall, and F1-score. Experimental results show that the LSTM model achieves high accuracy of 0.9974, with embedding dimension of 128 using 100 LSTM units, batch size of 64 and drop-out rate of 0.48. It is a substantial improvement over previous studies. The application of cross-validation further confirms the model’s reliability. This research demonstrates that the application of a fine-tuned LSTM network with robust data preprocessing can provide a powerful tool to combat online misinformation.
Co-Authors Armoogum, Sandhya Foogooa, Ravi Ravelonjara, Henri Michaël Sookarah, Doorgesh Suddul, Geerish