Early Detection of Phishing, Disinformation, and Extreme Opinions in Digital Text Using Transformer-Based Models - Wibowo, Munif; Faisal, Muhammad; Nugroho, Fresy (Lembaga Penelitian dan Pengabdian Masyarakat, 2026)
The rapid expansion of digital communication platforms has increased the circulation of phishing messages, disinformation, and extreme opinions, creating urgent challenges for cybersecurity and social stability. This study proposes a hybrid CNN–BiLSTM–Transformer framework for the early detection of harmful digital text. The model integrates convolutional feature extraction, sequential dependency learning, and self-attention mechanisms to capture local lexical patterns, contextual relations, and long-range semantic dependencies. Experimental evaluation was conducted using accuracy, precision, recall, F1-score, and ROC analysis, with CNN, LSTM, and RoBERTa used as baseline models. The proposed hybrid model achieved the highest classification accuracy of 95.0%, outperforming CNN (86.0%), LSTM (88.0%), and RoBERTa (91.0%). In addition, the model obtained 90.0% precision, 93.0% recall, and 91.5% F1-score, indicating a balanced ability to reduce false positives while maintaining strong detection sensitivity. Robustness testing further showed that the F1-score remained stable across normal, noisy, and adversarial text conditions, decreasing from 95.0% under normal conditions to 92.0% and 90.0% under noisy and adversarial settings, respectively. These findings demonstrate that the proposed hybrid Transformer-based architecture provides an effective and robust approach for supporting automated Cyber Early Warning Systems in detecting harmful digital content.