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Medical Named Entity Recognition from Indonesian Health-News using BiLSTM-CRF with Static and Contextual Embeddings Ignasius, Darnell; Novita Dewi , Ika; Bernadette Chayeenee Norman , Maria; Rakhmat Sani, Ramadhan
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11574

Abstract

Named Entity Recognition (NER) is vital for structuring medical texts by identifying entities such as diseases, symptoms, and drugs. However, research on Indonesian medical NER remain limited due to the lack of annotated corpora and linguistic resources. This scarcity often leads to difficulties in learning meaningful word representations, which are crucial for accurate entity identification. This research aims to compare the effectiveness of static and contextual embeddings in enhancing entity recognition on Indonesian biomedical text. The experimental setup involved utilizing both static (Word2Vec) and contextual (IndoBERT) embeddings in conjunction with neural architectures (BiLSTM) along with Conditional Random Fields (CRF). The BiLSTM architecture was selected for its ability to capture bidirectional dependencies in language sequences. Specifically, four models: Word2Vec-BiLSTM, Word2Vec-BiLSTM-CRF, IndoBERT-BiLSTM, and IndoBERT-BiLSTM-CRF were evaluated to assess the impact of contextual representations and structured decoding. The models were trained on a manually annotated DetikHealth corpus, where specific medical entities such as diseases, symptoms, and drugs were labeled with the BIO-tagging scheme. Performance was subsequently evaluated based on standard metrics: precision, recall, and F1-score. Results indicate that IndoBERT’s contextual embeddings significantly outperform static Word2Vec features. The IndoBERT-BiLSTM-CRF model achieved the highest performance micro-F1 0.4330, macro-F1 0.3297, with the Disease entity reaching an F1-score of 0.5882. Combining contextual embeddings with CRF-based decoding enhances semantic understanding and boundary consistency, demonstrating superior performance for Indonesian biomedical NER. Future work should explore domain-adaptive pretraining and larger biomedical corpora to further improve contextual accuracy.
Co-Authors ., Junta Zeniarza ., Junta Zeniarza Abdussalam Abdussalam, Abdussalam Abu Salam Agung Priyo Utomo, Rino Ahmad Khotibul Umam, Ahmad Khotibul Aisyah, Ade Nurul Al zami, Farrikh Alzami, Farrikh Ardytha Luthfiarta Arta Moro Sundjaja, Arta Moro Asih Rohmani Asih Rohmani Asih Rohmani, Asih Atha Rohmatullah, Fawwaz Bernadette Chayeenee Norman , Maria Budi Harjo Budi, Setyo Candra Irawan Catur Supriyanto Caturkusuma, Resha Meiranadi Christy Atika Sari Defri Kurniawan Defri Kurniawan Diana Aqmala Doheir, Mohamed Dwi Puji Prabowo, Dwi Puji Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erika Devi Udayanti Fahmi Amiq Farah Syadza Mufidah Farrikh Al Zami Farrikh Al Zami Fauzi Adi Rafrastara Florentina Esti Nilasari Florentina Esti Nilawati Guruh Fajar Shidik Hanny Haryanto Harun Al Azies Heru Lestiawan Hussein, Jasim Nadheer Hussein, Jassim Nadheer Ifan Rizqa Ignasius, Darnell Ika Novita Dewi Ikhwansyah Kurniawan Indra Gamayanto ISWAHYUDI ISWAHYUDI Ivan Bayu Fachreza Junta Zeniarja Karima, Nida Aulia Karin, Tan Regina Kiki Widia Kurniawan, Defri L. Budi Handoko Maszuda, Akbar Alvian Megantara, Rama Aria Melati Anggreni Sitorus Muhammad Naufal, Muhammad Nadya Azizah Novita Dewi , Ika Nugraha, Purwa Esti Pangesti, Galih Mentari Paramita, Cinantya Pergiwati, Dewi Pratiwi, Yunita Ayu Priyo Utomo, Rino Agung Pulung Nurtantio Andono Purwanto Purwanto Ramadhani, Dwi Arya Ricardus Anggi Pramunendar Richard Emmerig Rifa’i, Muhammad Nabhan S. Sukamto, Titien Sarker, Md. Kamruzzaman Sasono Wibowo Sendi Novianto Sendi Novianto Sendi Novianto Setyo Budi Setyo Budi Silla, Hercio Venceslau Sirait, Tamsir Hasudungan Sri Winarno Sri Winarno Suharnawi Suharnawi Suharnawi Suharnawi Suharnawi Sukamto, Titien S. Sukamto, Titien Suhartini Sulistyono, Teguh Syahrizal, Muhammad Iqbal Titien Suhartini Sukamto Titien Suhartini Sukamto Utomo, Danang Wahyu Wibowo, Isro' Rizky Wildanil Ghozi Wulan Puspita Loka Yani Parti Astuti Yanuaresta, Dianna Yupie Kusumawati Zahro, Azzula Cerliana Zami, Farrikh Al