Political Comperative Analysis of Indonesian Political Fake News Detection using IndoBERT-Bi-GRU-Attention Models: Evaluating Performance on Narratives and News Headlines Datasets
-
Juliana Damayanti Manurung
*
Universitas Mikroskil, Medan,
Indonesia
- Ronsen Purba Universitas Mikroskil, Medan, Indonesia (*) Corresponding Author
Abstract
The instant and massive spread of fake news on social media negatively impacts public trust in the media and news agencies. In politics, fake news is often used by politicians to gain support ahead of elections. Detecting fake news in Indonesia poses a significant challenge, especially for communities vulnerable to misinformation. This study aims to develop a new model that combines IndoBERT with Bi-GRU and Attention. Additionally, a comparison is made between the main model and two word embedding models, FastText and GloVe. The tests were conducted on datasets of headlines and news narratives separately. Data was sourced from CNN, Tempo.co, Kompas, and TurnBackHoax.ID. The results show that the IndoBERT-Bi-GRU-Attention model with FastText excelled on the headline dataset with an accuracy of 99.76% and an F1-Score of 99.61%, while the main IndoBERT-Bi-GRU-Attention model excelled on the narrative dataset with an accuracy of 99.08% and an F1-Score of 98.40%. This research demonstrates that IndoBERT can be combined with Bi-GRU, significantly contributing to the development of fake news detection models.
Downloads
References
D. S. M and Hairunnisa, “The Phenomenon of Fake News (Hoax) in Mass Communication: Causes, Impacts, and Solutions,” Open Access Indonesia Journal of Social Sciences, vol. 6, no. 3, pp. 980–988, May 2023, doi: 10.37275/oaijss.v6i3.161.
“Sampai Mei 2023, Kominfo Identifikasi 11.642 Konten Hoaks – Ditjen Aptika.” Accessed: May 02, 2024. [Online]. Available: https://aptika.kominfo.go.id/2023/06/sampai-mei-2023-kominfo-identifikasi-11-642-konten-hoaks/
A. A. Kurniawan and M. Mustikasari, “Implementasi Deep Learning Menggunakan Metode CNN dan LSTM untuk Menentukan Berita Palsu dalam Bahasa Indonesia,” Jurnal Informatika Universitas Pamulang, vol. 5, no. 4, pp. 2622–4615, 2020, doi: 10.32493/informatika.v5i4.7760.
J. Amalia, J. Pakpahan, M. Pakpahan, Y. Panjaitan, F. Informatika dan Teknik Elektro, and I. Teknologi Del, “Model Klasifikasi Berita Palsu Menggunakan Bidirectional LSTM Dan Word2Vec Sebagai Vektorisasi,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 9, no. 4, 2022
R. Adipradana, B. P. Nayoga, R. Suryadi, and D. Suhartono, “Hoax analyzer for indonesian news using rnns with fasttext and glove embeddings,” Bulletin of Electrical Engineering and Informatics, vol. 10, no. 4, pp. 2130–2136, Aug. 2021, doi: 10.11591/eei.v10i4.2956.
A. Karen, M. Christopher, N. N. Qomariyah, I. B. K. Manuaba, and A. K. Anom, “Clarifact-AI: Detecting Fake News in Indonesian Language with Natural Language Processing Using BiLSTM and IndoBERT Models,” in 10th International Conference on ICT for Smart Society, ICISS 2023 - Proceeding, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/ICISS59129.2023.10291714.
M. Rifky Maulana et al., “Penerapan Metode Lstm Dalam Pembuatan Sistem Pendeteksi Berita Palsu Berbahasa Indonesia,” Syntax : Jurnal Informatika, vol. 12, no. 02, pp. 43–51, Nov. 2023, doi: 10.35706/SYJI.V12I02.9924.
T. E. Trueman, A. K. J., P. Narayanasamy, and J. Vidya, “Attention-based C-BiLSTM for fake news detection,” Appl Soft Comput, vol. 110, Oct. 2021, doi: 10.1016/j.asoc.2021.107600.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate,” Sep. 2014, [Online]. Available: http://arxiv.org/abs/1409.0473
S. Sarica and J. Luo, “Stopwords in Technical Language Processing,” PLoS One, vol. 16 8, no. 8 August, Aug. 2021, doi: 10.1371/JOURNAL.PONE.0254937.
“Word Embedding dengan FastText – Master of Computer Science.” Accessed: May 06, 2024. [Online]. Available: https://mti.binus.ac.id/2021/12/31/word-embedding-dengan-fasttext/
J. Choi and S. W. Lee, “Improving FastText with inverse document frequency of subwords,” Pattern Recognit Lett, vol. 133, pp. 165–172, May 2020, doi: 10.1016/j.patrec.2020.03.003.
J. Xu and Q. Du, “A deep investigation into fasttext,” in Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019, Institute of Electrical and Electronics Engineers Inc., Aug. 2019, pp. 1714–1719. doi: 10.1109/HPCC/SmartCity/DSS.2019.00234.
J. Devlin, M.-W. Chang, K. Lee, K. T. Google, and A. I. Language, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.” [Online]. Available: https://github.com/tensorflow/tensor2tensor
F. Koto, A. Rahimi, J. H. Lau, and T. Baldwin, “IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP,” Online. [Online]. Available: https://huggingface.co/
F. Koto Jey Han Lau Timothy Baldwin, “INDOBERTWEET: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization.” [Online]. Available: https://huggingface.co/huseinzol05/
A. B. Y. A. Putra, Y. Sibaroni, and A. F. Ihsan, “Disinformation Detection on 2024 Indonesia Presidential Election using IndoBERT,” in 2023 International Conference on Data Science and Its Applications, ICoDSA 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 350–355. doi: 10.1109/ICoDSA58501.2023.10277572.
“indo-bert-news-classification/transformer.png at master · rubentea16/indo-bert-news-classification · GitHub.” Accessed: May 07, 2024. [Online]. Available: https://github.com/rubentea16/indo-bert-news-classification/blob/master/transformer.png
B. Juarto and Yulianto, “Indonesian News Classification Using IndoBert,” International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 2, pp. 454–460, Feb. 2023, Accessed: Feb. 13, 2025. [Online]. Available: https://ijisae.org/index.php/IJISAE/article/view/2654
S. Narkhede, “Understanding Confusion Matrix.” Accessed: May 08, 2024. [Online]. Available: https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62
J. Xu, Y. Zhang, and D. Miao, “Three-way confusion matrix for classification: A measure driven view,” Inf. Sci., vol. 507, pp. 772–794, Jan. 2020, doi: 10.1016/J.INS.2019.06.064.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Political Comperative Analysis of Indonesian Political Fake News Detection using IndoBERT-Bi-GRU-Attention Models: Evaluating Performance on Narratives and News Headlines Datasets
Pages: 2459-2468
Copyright (c) 2025 Juliana Damayanti Manurung, Ronsen Purba
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
