Performance Improvement of Fake News Detection Models Using Long Short-Term Memory Hyperparameter Optimization
(1) Department of Electrical Engineering, Politeknik Negeri Sriwijaya, Indonesia
(2) Department of Electrical Engineering, Politeknik Negeri Sriwijaya, Indonesia
(3) Department of Electrical Engineering, Politeknik Negeri Sriwijaya, Indonesia
(4) Department of Electronics Engineering, National Chin-yi University of Technology, Taiwan
Abstract
Purpose: The proposed model was developed based on prior research that distinguished between fake and real news using a deep learning-based methodology and an LSTM neural network, with a model accuracy of 99.88%. This study uses hyperparameter tuning techniques on a Long Short-Term Long Memory (LSTM) neural network architecture to improve the accuracy of a fake news detection model.
Methods: To improve the accuracy of the fake news detection model and optimize the model from previous research, this study uses the hyperparameter tuning technique on models with Long Short-Term Memory (LSTM) neural network architecture. For this technique, three different types of experiments, hyperparameter tuning on the LSTM layer, Dense layer, and Optimizer, were conducted to obtain the best hyperparameters in each layer of the model architecture and the model parameters proposed. The fake and real news dataset, which has also been used in earlier studies, was used in this study.
Results: The proposed model could detect fake news with a high accuracy of 99.97%, surpassing the previous research models with an accuracy of 99.88%.
Novelty: The novelty of this study was the hyperparameter tuning technique on different layers of the LSTM neural network to optimize the fake news detection model. The research aims to improve upon previous approaches and increase the accuracy of the model.
Keywords
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