Comparative Analysis of IndoBERT and mBERT for Online Gambling Comment Detection in Indonesian Social Media

Satria Adi Nugraha; Citra Lestari; Karyna Budi Sanjaya; Rafi Abhista Naya; Jocelyn Jolie

doi:10.52436/1.jutif.2026.7.2.5677

Authors

Satria Adi Nugraha School of Information Technology, Universitas Ciputra, Indonesia
Citra Lestari School of Information Technology, Universitas Ciputra, Indonesia
Karyna Budi Sanjaya School of Information Technology, Universitas Ciputra, Indonesia
Rafi Abhista Naya School of Information Technology, Universitas Ciputra, Indonesia
Jocelyn Jolie School of Information Technology, Universitas Ciputra, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2026.7.2.5677

Keywords:

BERT, Cybercrime Detection, IndoBERT, MBERT, Social Media Analysis, Transformer Models

Abstract

The rapid growth of illegal online gambling promotions in Indonesian social media comments requires automated detection systems capable of handling informal and noisy text. This study aims to evaluate the effectiveness of Transformer-based language models for detecting online gambling-related comments in Indonesian Twitter and YouTube data. Two pre-trained models, IndoBERT and mBERT, were fine-tuned and compared using a labeled dataset consisting of gambling and non-gambling comments. Model performance was evaluated using accuracy, precision, recall, and F1-score. Experimental results show that IndoBERT achieved 98% accuracy and F1-score, outperforming mBERT, which achieved 96% on the same dataset. Additionally, performance was compared against a recurrent neural network baseline to validate the effectiveness of Transformer-based architectures. The findings demonstrate that language-specific pre-training provides measurable advantages for detecting domain-specific content in Indonesian social media. This study contributes empirical evidence supporting the application of Transformer models for automated moderation of harmful online content in Indonesian digital platforms.

Downloads

Download data is not yet available.

References

A. S. Wardani, “Makin Gawat, 960 Ribu Pelajar dan Mahasiswa Terjerat Judi Online,” Liputan6.com. Accessed: Oct. 06, 2025. [Online]. Available: https://www.liputan6.com/tekno/read/5799064/makin-gawat-960-ribu-pelajar-dan-mahasiswa-terjerat-judi-online

N. Azka Syakira, N. Fathma Ramadhahana, N. Devi Anggita, T. Tsaqifa, and R. Naila Husna, “Dampak Konsumerisme Berupa Judi Online di Indonesia: Perspektif Ekonomi, Sosial, dan Mental,” Jurnal Interaktif, vol. 16, no. 2, pp. 73–79, Dec. 2024, doi: 10.21776/ub.interaktif.2024.016.02.3.

S. Sriyana, “JUDI ONLINE: DAMPAK SOSIAL, EKONOMI, DAN PSIKOLOGIS DI ERA DIGITAL,” JURNAL SOCIOPOLITICO, vol. 7, no. 1, pp. 27–34, Feb. 2025, doi: 10.54683/sociopolitico.v7i1.169.

A. Price, “Online Gambling in the Midst of COVID-19: A Nexus of Mental Health Concerns, Substance Use and Financial Stress,” International Journal of Mental Health and Addiction, vol. 20, no. 1, pp. 362–379, Feb. 2022, doi: 10.1007/s11469-020-00366-1.

A. Sirola, N. Savela, I. Savolainen, M. Kaakinen, and A. Oksanen, “The Role of Virtual Communities in Gambling and Gaming Behaviors: A Systematic Review,” J Gambl Stud, vol. 37, no. 1, pp. 165–187, Mar. 2021, doi: 10.1007/s10899-020-09946-1.

R. Suriá-Martínez, F. García-Castillo, E. Villegas-Castrillo, C. López-Sánchez, and C. Carretón-Ballester, “Negative impact of online gambling problematic in disabled and non-disabled university students: exploring the risk profile,” Front Psychol, vol. 15, Sep. 2024, doi: 10.3389/fpsyg.2024.1429122.

Populix, “Understanding the impact of online gambling ads exposure,” Populix. [Online]. Available: https://info.populix.co/product/consumer-trend-report

E. Bolat, C. Panourgia, A. Yankouskaya, and M. Kelly, “Influencer-Driven Gambling Content and Its Impact on Children and Young People: A Scoping Study,” Curr Addict Rep, vol. 12, no. 1, p. 3, Jan. 2025, doi: 10.1007/s40429-025-00616-z.

Sudirham and T. B. Sari, “Adapting counter-gambling advertising to the Indonesian context: a call to action,” J Public Health (Bangkok), vol. 47, no. 2, pp. e246–e247, May 2025, doi: 10.1093/pubmed/fdae221.

Z. K. Muharam, W. Astuti, R. Prasida, and D. Syahputra, “Indonesian Journal of Digital Public Relations (IJDPR) PENGGUNAAN INFLUENCER DALAM PROMOSI JUDI ONLINE DAN SENTIMEN PUBLIK THE USE OF INFLUENCERS IN PROMOTION ONLINE GAMBLING AND PUBLIC SENTIMENT,” 2024. [Online]. Available: https://journals.telkomuniversity.ac.id/IJDPR

Kumparan News, “Marak iklan judol di YouTube, apa kata Google Indonesia?,” Kumparan. [Online]. Available: https://kumparan.com/kumparannews/marak-iklan-judol-di-youtube-apa-kata-google-indonesia-24WaAnoJ08G/

N. C. Harriott and A. L. Ryan, “Proteomic profiling identifies biomarkers of COVID-19 severity,” Heliyon, vol. 10, no. 1, p. e23320, Jan. 2024, doi: 10.1016/j.heliyon.2023.e23320.

E. Benavides-Astudillo, W. Fuertes, S. Sanchez-Gordon, D. Nuñez-Agurto, and G. Rodríguez-Galán, “A Phishing-Attack-Detection Model Using Natural Language Processing and Deep Learning,” Applied Sciences, vol. 13, no. 9, p. 5275, Apr. 2023, doi: 10.3390/app13095275.

R. A. Fitrianto, A. S. Editya, M. M. Alamin, A. L. Pramana, and A. K. Alhaq, “Classification of Indonesian Sarcasm Tweets on X Platform Using Deep Learning,” in 2024 7th International Conference on Informatics and Computational Sciences (ICICoS), IEEE, Jul. 2024, pp. 388–393. doi: 10.1109/ICICoS62600.2024.10636904.

S. Oh, K. Chung, and J. Choi, “Resource-Oriented Augmentation of a Train Timetable,” IEEE Access, vol. 11, pp. 114283–114290, 2023, doi: 10.1109/ACCESS.2023.3323590.

M. Y. Ali, A. M. Yimer, and T. S. Dessie, “An empirical estimation of aggregate import demand under foreign exchange constraints: Evidence from Ethiopia,” PLoS One, vol. 19, no. 6, p. e0303587, Jun. 2024, doi: 10.1371/journal.pone.0303587.

R. B. Perdana, A. -, I. Budi, A. B. Santoso, A. Ramadiah, and P. K. Putra, “Detecting Online Gambling Promotions on Indonesian Twitter Using Text Mining Algorithm,” International Journal of Advanced Computer Science and Applications, vol. 15, no. 8, 2024, doi: 10.14569/IJACSA.2024.0150893.

K. Kamdan, M. P. Anugrah, M. J. Almutaali, R. Ramdani, and I. L. Kharisma, “Performance Analysis of IndoBERT for Detection of Online Gambling Promotion in YouTube Comments,” in The 7th International Global Conference Series on ICT Integration in Technical Education & Smart Society, Basel Switzerland: MDPI, Sep. 2025, p. 66. doi: 10.3390/engproc2025107066.

A. S. Maldini, W. S. J. Saputra, and D. A. Prasetya, “Multimodal Detection of Covert Online Gambling Advertisements Using Faster R-CNN and Tr-OCR,” bit-Tech, vol. 8, no. 1, pp. 953–963, Aug. 2025, doi: 10.32877/bt.v8i1.2769.

K. A. Adriana and E. B. Setiawan, “Enhancing Cyberbullying Detection with a CNN-GRU Hybrid Model, Word2Vec, and Attention Mechanism,” Jurnal Teknik Informatika (Jutif), vol. 6, no. 3, pp. 1113–1130, Jun. 2025, doi: 10.52436/1.jutif.2025.6.3.4176.

Z. Fang, H. Zhang, J. He, Z. Qi, and H. Zheng, “Semantic and Contextual Modeling for Malicious Comment Detection with BERT-BiLSTM,” arXiv preprint arXiv:2503.11084, 2025. doi: 10.48550/arXiv.2503.11084.

M. F. Cahyadi and T. H. Rochadiani, “Implementasi Ensemble Deep Learning Untuk Analisis Sentimen Terhadap Genre Game Mobile,” JURNAL MEDIA INFORMATIKA BUDIDARMA, vol. 8, no. 3, p. 1512, Jul. 2024, doi: 10.30865/mib.v8i3.7832.

Teddy Oswari, Murniyati, Trityanti Yusnitasari, Nurasiah, and Seviyanti Wijay, “Sentiment Analysis of Indonesian YouTube Reviews About Lesbian, Gay, Bisexual, and Transgender (LGBT) using IndoBERT Fine Tuning,” Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, vol. 15, no. 01, pp. 26–37, Oct. 2025, doi: 10.24843/LKJITI.2024.v15.i01.p03.

Sofyan Hidayat, Nining Rahaningsih, Raditya Danar Dana, and Mulyawan, “Improvement of User Sentiment Classification Model for the Indomaret Poinku Application Using the Naïve Bayes Method,” Journal of Artificial Intelligence and Engineering Applications (JAIEA), vol. 4, no. 2, pp. 1497–1500, Feb. 2025, doi: 10.59934/jaiea.v4i2.937.

M. F. Juna and M. Hayaty, “The observed preprocessing strategies for doing automatic text summarizing,” Computer Science and Information Technologies, vol. 4, no. 2, pp. 119–126, Jul. 2023, doi: 10.11591/csit.v4i2.p119-126.

M. Ashmawy, M. W. Fakhr, and F. A. Maghraby, “Lexical Normalization Using Generative Transformer Model (LN-GTM),” International Journal of Computational Intelligence Systems, vol. 16, no. 1, p. 183, Nov. 2023, doi: 10.1007/s44196-023-00366-8.

S. Sarica and J. Luo, “Stopwords in technical language processing,” PLoS One, vol. 16, no. 8, p. e0254937, Aug. 2021, doi: 10.1371/journal.pone.0254937.

Rianto, A. B. Mutiara, E. P. Wibowo, and P. I. Santosa, “Improving the accuracy of text classification using stemming method, a case of non-formal Indonesian conversation,” Journal of Big Data, vol. 8, no. 1, p. 26, Dec. 2021, doi: 10.1186/s40537-021-00413-1.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2019), Minneapolis, MN, USA, 2019, pp. 4171–4186. doi: 10.18653/v1/N19-1423.

N. Surantha, P. Atmaja, David, and M. Wicaksono, “A Review of Wearable Internet-of-Things Device for Healthcare,” Procedia Computer Science, vol. 179, pp. 936–943, 2021, doi: 10.1016/j.procs.2021.01.083.

Dhendra and V. Gayuh Utomo, “Benchmarking IndoBERT and Transformer Models for Sentiment Classification on Indonesian E-Government Service Reviews,” Jurnal Transformatika, vol. 23, no. 1, pp. 86–95, Jul. 2025, doi: 10.26623/transformatika.v23i1.12095.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” May 2019.

Z. Lin et al., “Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking,” in Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Stroudsburg, PA, USA: Association for Computational Linguistics, 2021, pp. 5640–5648. doi: 10.18653/v1/2021.naacl-main.448.