Fake news detection in the 2024 Indonesian general election using Bidirectional Long Short-Term Memory (BI-LSTM) algorithm

Arkaan, Shabiq Ghazi and Atmadja, Aldy Rialdy and Firdaus, Muhammad Deden (2024) Fake news detection in the 2024 Indonesian general election using Bidirectional Long Short-Term Memory (BI-LSTM) algorithm. Fake news detection in the 2024 Indonesian general election using Bidirectional Long Short-Term Memory (BI-LSTM) algorithm, 21 (2). pp. 22-30. ISSN 2654-3990

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Abstract

The advancement of information technology provides convenience, but it also brings about problems. One area affected by this is the election process in Indonesia, which has seen a rise in fake news often used to discredit political opponents. Fake news misleads the public into believing incorrect information related to the election. To address this issue, a system is needed to detect fake news in the 2024 election to help the public differentiate between true and false information. This system is developed using an artificial intelligence and deep learning approach trained to do text classification on fake news detection. The training data consists of 1999 entries obtained from the Global Fact-Check Database from turnbackhoax.id, detik.com, and cnnindonesia.com. The machine learning model is built using the Bidirectional Long Short-Term Memory (BI-LSTM) algorithm, which is suitable for processing text data. This study compares two types of feature representations: TF-IDF and contextual embeddings with the IndoBERT model. The study results in the best model for text classification with an accuracy of 92% and a loss of 42.92%, achieved by the model using TF-IDF feature representation. The implementation of this system aims to enhance the integrity of the election process by minimizing the spread of misinformation. Future work will focus on refining the model and expanding the dataset to include more diverse sources for improved accuracy and robustness.

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