Jolie, Jocelyn
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Comparative Analysis of IndoBERT and mBERT for Online Gambling Comment Detection in Indonesian Social Media Nugraha, Satria Adi; Lestari, Citra; Sanjaya, Karyna Budi; Naya, Rafi Abhista; Jolie, Jocelyn
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5677

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.