Improving Fake News Detection Accuracy with Lexicon-based Approach and LSTM through Text Preprocessing - Mashuri, Chamdan; Prastyo, Edwin Hari Agus; Hariri, Fajar Rohman (Diponegoro University, 2025)
Fake news detection has become a critical issue in the digital era, especially with the rapid growth of social media and online platforms. This research aims to enhance the accuracy of detecting fake news in Indonesian by developing a model using lexicon-based and Long Short-Term Memory (LSTM) approaches. The study integrates sentiment analysis with lexicon-based scoring to identify key features in news articles, while LSTM is employed to analyze sequential patterns in the data. The methods were tested on a dataset consisting of both hoax and non-hoax news collected from reliable sources. The results indicate that the hybrid model significantly improves the detection accuracy, achieving an impressive accuracy rate of 99%. This research demonstrates the potential of combining lexicon-based and LSTM approaches to overcome challenges in detecting fake news, especially in low-resource languages like Indonesian. The findings contribute to advancing the development of reliable and efficient systems for combating misinformation in the digital age.