Optimalisasi Model SciBERT dengan Attention-BiLSTM-CRF untuk Pengenalan Entitas Penyakit dalam Teks Biomedis - Pamungkas, Tahta Arya; Salam, Abu (Building of Informatics, Technology and Science (BITS), 2025)
This research aims to improve the performance of medical entity recognition in biomedical text by modifying the SciBERT model with Attention-BiLSTM-CRF. Although SciBERT, based on the BERT architecture and trained on biomedical text data, has proven effective in entity recognition, it still has limitations in handling complex medical entities, especially nested entities. As a solution, this research integrates Attention, BiLSTM, and CRF components into the SciBERT model to enhance entity recognition accuracy. Experimental results show that the SciBERT + Attention-BiLSTM-CRF model outperforms the SciBERT model across all key evaluation metrics. Precision improved by 1.7% (from 0.8221 to 0.8364), Recall increased by 2.9% (from 0.8537 to 0.8768), and F1-Score increased by 2.1% (from 0.8372 to 0.8554). These improvements demonstrate that this modification significantly enhances the model's ability to recognize more complex medical entities in biomedical text. The addition of Attention and BiLSTM enriches contextual understanding, while CRF ensures consistency across entity labels. These results indicate that this approach could significantly contribute to automated systems in processing medical data.