Fake News Detection in the 2024 Indonesian General Election Using Bidirectional Long Short-Term Memory (BI-LSTM) Algorithm
Abstract
Keywords
References
M. Ngafifi, “Kemajuan Teknologi Dan Pola Hidup Manusia Dalam Perspektif Sosial Bu- daya,” J. Pembang. Pendidik. Fondasi dan Apl., vol. 2, no. 1, pp. 33–47, 2014, doi: 10.21831/jppfa.v2i1.2616.
A. S. Cahyono, “Pengaruh Media Sosial terhadap Penyebaran Hoax oleh Digital Native,” Pengaruh media Sos. terhadap perubahan Sos. Masy. di Indones., vol. 1, no. 1, pp. 140–157, 2016.
C. Juditha, “Hoax Communication Interactivity in Social Media and Anticipation (Interaksi Komunikasi Hoax di Media Sosial serta Antisipasinya),” J. Pekommas, vol. 3, no. 1 SE-Communication, pp. 31–44, Sep. 2018, doi: 10.30818/jpkm.2018.2030104.
MASTEL, “Hasil Survey Wabah HOAX Nasional 2017 | MASTEL Living Enabler.” Accessed:
May 23, 2024. [Online]. Available: https://mastel.id/hasil-survey-wabah-hoax-nasional-2017/
R. Anisa and R. Rachmaniar, “Hoax Politik Pada Media Sosial Instagram (Studi Etnografi Virtual Tentang Keberadaan Instagram dan Hoax Politik),” Pros. Komun., vol. 1, no. 2, 2017.
M. Rama, D. Sulistyo, Fatma, and U. Najicha, “PENGARUH BERITA HOAX TERHADAP KESATUAN DAN PERSATUAN BANGSA INDONESIA,” J. Kewarganegaraan, vol. 6, no. 1, 2022.
O. N. Julianti et al., “PENERAPAN NATURAL LANGUAGE PROCESSING PADA ANAL- ISIS SENTIMEN JUDI ONLINE DI MEDIA SOSIAL TWITTER,” JATI J. Mhs. Tek. In-
form., vol. 8, no. 3, pp. 2936–2941, 2024.
D. Khurana, A. Koli, K. Khatter, and S. Singh, “Natural language processing: state of the art, current trends and challenges,” Multimed. Tools Appl., vol. 82, no. 3, pp. 3713–3744, 2023, doi: 10.1007/s11042-022-13428-4.
D. Sanjaya and S. Budi, “Prediksi Pencapaian Target Kerja Menggunakan Metode Deep Learning dan Data Envelopment Analysis,” J. Tek. Inform. dan Sist. Inf., vol. 6, no. 2, pp. 288–300, 2020, doi: 10.28932/jutisi.v6i2.2678.
M. Soori, B. Arezoo, and R. Dastres, “Artificial intelligence, machine learning and deep learning in advanced robotics, a review,” Cogn. Robot., vol. 3, pp. 54–70, 2023, doi: https://doi.org/10.1016/j.cogr.2023.04.001.
X. Dong and L. Qian, “Semi-supervised bidirectional RNN for misinformation de- tection,” Mach. Learn. with Appl., vol. 10, no. September, p. 100428, 2022, doi: 10.1016/j.mlwa.2022.100428.
B. P. Nayoga, R. Adipradana, R. Suryadi, and D. Suhartono, “Hoax Analyzer for Indonesian News Using Deep Learning Models,” Procedia Comput. Sci., vol. 179, no. 2020, pp. 704–712, 2021, doi: 10.1016/j.procs.2021.01.059.
J. Alghamdi, Y. Lin, and S. Luo, “A Comparative Study of Machine Learning and Deep Learning Techniques for Fake News Detection,” Inf., vol. 13, no. 12, pp. 1–28, 2022, doi: 10.3390/info13120576.
A. A. Kurniawan and M. Mustikasari, “Implementasi Deep Learning Menggunakan Metode CNN dan LSTM untuk Menentukan Berita Palsu dalam Bahasa Indonesia,” J. Inform. Univ. Pamulang, vol. 5, no. 4, pp. 2622–4615, 2020, doi: 10.32493/informatika.v5i4.7760.
X. Li, Y. Xia, X. Long, Z. Li, and S. Li, “Exploring Text-Transformers in AAAI 2021 Shared Task: COVID-19 Fake News Detection in English,” Commun. Comput. Inf. Sci., vol. 1402 CCIS, pp. 106–115, 2021, doi: 10.1007/978-3-030-73696-5_11.
D. R. Alghifari, M. Edi, and L. Firmansyah, “Implementasi Bidirectional LSTM untuk Anali- sis Sentimen Terhadap Layanan Grab Indonesia,” J. Manaj. Inform., vol. 12, no. 2, pp. 89–99, 2022, doi: 10.34010/jamika.v12i2.7764.
R. Siringoringo, J. Jamaluddin, R. Perangin-angin, E. J. G. Harianja, G. Lumbantoruan, and
E. N. Purba, “Model Bidirectional Lstm Untuk Pemrosesan Sekuensial Data Teks Spam,” METHOMIKA J. Manaj. Inform. dan Komputerisasi Akunt., vol. 7, no. 2, pp. 265–271, 2023, doi: 10.46880/jmika.vol7no2.pp265-271.
Dewan Pers, “Data Perusahaan Pers.” Accessed: May 23, 2024. [Online]. Available: https://dewanpers.or.id/data/perusahaanpers
M. C. Untoro and M. A. N. M. Yusuf, “Evaluate of Random Undersampling Method and Majority Weighted Minority Oversampling Technique in Resolve Imabalanced Dataset,” IT
J. Res. Dev., vol. 8, no. 1, pp. 1–13, 2023, doi: 10.25299/itjrd.2023.12412.
M. Zheng et al., “UFFDFR: Undersampling framework with denoising, fuzzy c-means cluster- ing, and representative sample selection for imbalanced data classification,” Inf. Sci. (Ny)., vol. 576, pp. 658–680, 2021, doi: https://doi.org/10.1016/j.ins.2021.07.053.
V. Lendave, “A Guide to Text Preprocessing Using BERT,” analyticsindiamag. Accessed: May 23, 2024. [Online]. Available: https://analyticsindiamag.com/a-guide-to-text-preprocessing- using-bert/
M. N. A. Putera Khano, D. R. S. Saputro, S. Sutanto, and A. Wibowo, “Sentiment Analysis With Long-Short Term Memory (Lstm) and Gated Recurrent Unit (Gru) Algo- rithms,” BAREKENG J. Ilmu Mat. dan Terap., vol. 17, no. 4, pp. 2235–2242, 2023, doi: 10.30598/barekengvol17iss4pp2235-2242.
D. Rengasamy, M. Jafari, B. Rothwell, X. Chen, and G. Figueredo, “Deep Learning With Dynamically Weighted Loss Function for Sensor-based Prognostics and Health Management,” Sensors, Jan. 2020, doi: 10.3390/s20030723.
M. Fahmy Amin, “Confusion Matrix in Three-class Classification Problems: A Step-by-Step Tutorial,” J. Eng. Res., vol. 7, no. 1, pp. 0–0, 2023, doi: 10.21608/erjeng.2023.296718.
X. Ying, “An Overview of Overfitting and its Solutions,” J. Phys. Conf. Ser., vol. 1168, no. 2, 2019, doi: 10.1088/1742-6596/1168/2/022022.
T. Islam, Hosen, Md Alamin, A. Mony, M. T. Hasan, I. Jahan, and A. Kundu, “A Proposed BI- LSTM Method to Fake News Detection,” 2022 International Conference for Advancement in Technology, ICONAT 2022, 2022, doi: https://doi.org/10.1109/ICONAT53423.2022.9725937.
DOI: 10.33751/komputasi.v21i2.9987
![](/public/site/images/putik/Graph.png)
![](/public/site/images/putik/pdf-icon.png)
Refbacks
- There are currently no refbacks.
![Creative Commons License](http://licensebuttons.net/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.