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All Journal Jurnal Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI EXPLORE CCIT (Creative Communication and Innovative Technology) Journal International Journal of Informatics and Computation EXPLORE Jurnal Teknik Informatika dan Teknologi Informasi Safari : Jurnal Pengabdian Masyarakat Indonesia Journal Of Computer Science And Technology Jurnal Rekayasa Sistem Informasi dan Teknologi Journal of Computer Science and Information Technology Jurnal Kecerdasan Buatan dan Teknologi Informasi Journal of Data Analytics, Information, and Computer Science (JDAICS) Jurnal Computer and Technology Explore
Suryadi, Emi
Universitas Teknologi Mataram
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Journal : International Journal of Informatics and Computation

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Sentiment Analysis of a 271 Trillion Rupiahs Corruption Case Using LSTM Selamet Riadi; Rudi Muslim; Emi Suryadi; Karina Nurwijayanti; M. Zulpahmi; Muhamad Masjun Efendi; Bahtiar Imran
International Journal of Informatics and Computation Vol. 7 No. 1 (2025): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v7i1.104

Abstract

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 rupiah 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. The model 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 rupiah corruption issues.
Co-Authors ahmad yani Ahmad Yani Ahmad Yani Akbar, Ardiyallah Ardiyallah Akbar Ardiyallah Akbar Arwidiyarti, Dwinita Aspari, Roni Astutik Zaerani Aulia, Asma Ayu Awanda Aerin Maesyarani Beni Ari Hidayatulloh Beni Ary Hidayatullah Buchtami, Lalu Reza Dedi Supriadi Diki Hananta Firdaus Efendi, Muhamad Masjun Ema Zulfaningsih Erfan Wahyudi Fadlun Mudin Giardi, Muh Hamzah Andung Hamzan Wadi Henni Comala Hikmi Imran, Bahtiar Juniyardi, Lalu Karim, Muh Nasirudin Karina Nurwijayanti Karina Nurwijayanti Lalu Darmawan Bakti Lalu Delsi Samsumar, M.Eng. Lalu Efendi Novian Lalu Moh. Nurkholis M. Zulpahmi Maspaeni Maspaeni Moh Subli Moh. Subli Mudin, Fadlun Nining Putri Ningsih Novian, Lalu Efendi NUR FAUZIAH Nurkholis, Lalu Moh. Nurul Yakin Nurul Yakin Nurul Yakin Rudi Muslim Rudi Muslim Said, Syukroni Selamet Riadi Selamet Riadi Subli Kerta Yan Santosa Yumarlin MZ Zaeniah Zaeniah Zaenudin Zaenudin Zaenudin Zaenudin Zaenudin Zulpahmi, M. Zulpan Hadi