Comparison of Text Classification Techniques in Fake News Detection in the Digital Information Age

Dimas Muhammad Ilham; Sri  Mujiyono

doi:10.59395/ijadis.v6i1.1365

Authors

Dimas Muhammad Ilham Faculty of Technology and Information, Universitas Ngudi Waluyo
Sri Mujiyono Faculty of Technology and Information, Universitas Ngudi Waluyo

DOI:

https://doi.org/10.59395/ijadis.v6i1.1365

Keywords:

Accuracy, Comparison of text classification techniques, Convolutional Neural Networks (CNN), Deep Learning, F1-score, Fake news, Precision, Recall, Recurrent Neural Networks (RNN)

Abstract

A comparison of text classification techniques for detecting fake news in the digital information age has been discussed in this study, with a focus on the application of Deep Learning methods, specifically Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The increasing spread of fake news through digital platforms emphasizes the importance of developing effective methods for identifying inaccurate information. In this study, a news dataset was collected from various sources, and both models were applied for text classification analysis. The performance of the model was then measured based on accuracy, precision, recall, and F1-score. The results showed that although both have their own advantages, better results in terms of processing speed and classification accuracy were found in CNN compared to RNN. These findings provide important insights for the development of more efficient and effective fake news detection systems in the digital age.

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