Sentiment Analysis of a 271Trillion RupiahsCorruption Case Using LSTM

Sentiment Analysis of a 271Trillion RupiahsCorruption Case Using LSTM

Sentiment Analysis, Long Short-term Memory, Corruption

Corruption is one of the most pressing issues in Indonesia, significantly affecting public trust in governance and the nation’s development. Among the many corruption cases that have surfaced, the recent 271 trillion rupiahs corruption case has drawn widespread attention and public discourse. Understanding the public's perception and sentiment regarding such cases can provide valuable insights into how these issues impact society. Researchers identified an opportunity to leverage sentiment analysis as a method to capture and analyze public sentiment in this context. The dataset for this study was collected from the social media platform Twitter (X) using a data crawling technique. Prior to analysis, preprocessing was performed to clean and prepare the data. After preprocessing, the data was categorized into three sentiment labels: negative, positive, and neutral. To perform sentiment classification, this study utilized the LSTM (Long Short-Term Memory) algorithm, a deep learning method particularly suited for sequential data analysis. Themodel was trained over a total of 10 epochs. The classification results demonstrated that the LSTM algorithm achieved an accuracy of 0.9365 at the 10th epoch, showcasing its effectiveness in analyzing public sentiment regarding 271 trillion rupiahs corruption issues

Selamet Riadi1, Rudi Muslim2, Emi Suryadi3, Karina Nurwijayanti4, M. Zulpahmi5, Muhamad Masjun Efendi6, Bahtiar Imran

https://ijicom.respati.ac.id/index.php/ijicom/article/view/104/69

2025

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