Optimizing Convolutional Neural Networks with Particle Swarm Optimization for Enhanced Hoax News Detection
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Background: The global spreading of hoax news is causing significant challenges, by misleading the public and undermining public trust in media and institutions. This issue is worsened by the rapid spreading of misinformation which is facilitated by digital platforms, triggering social unrest and threatening national security. To overcome this problem, reliable and robust method is essential to adapt to the evolving tactics of misleading information spreading.
Objective: This study aimed to improve the accuracy of hoax news detection tools by evaluating the effectiveness of Deep Learning methods enhanced with Convolutional Neural Networks (CNNs) using Particle Swarm Optimization (PSO).
Methods: The dataset was processed by tokenization, stopword removal, and stemming. CNNs were trained with default parameters, due to their potential as one of the effective methods for text classification. Furthermore, PSO was used to optimize the main parameters such as filters, kernel sizes, and learning rate, which was refined iteratively based on validation accuracy.
Results: The optimized CNNs+PSO was further tested by data training to show its effectiveness in detecting hoax news and misleading articles. The result showed that the optimized CNNs+PSO model had high effectiveness, by achieving accuracy rate of 92.06%, precision 91.6%, and recall 96.19%. These values validated the model’s ability to classify hoax news in Indonesian accurately.
Conclusion: This study showed that the optimized CNNs+PSO method was highly effective in detecting hoax news and misleading articles by achieving impressive accuracy, precision, and recall rate. The integration showed the potential of CNNs+PSO to mitigate the impacts of hoax news, enhance public awareness, and promote people to critically believe the news
Keywords: Convolutional Neural Networks, Deep Learning, Hoax, Particle Swarm Optimization, Text Mining
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