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Journal : JOIV : International Journal on Informatics Visualization

Entity Extraction in Indonesian Online News Using Named Entity Recognition (NER) with Hybrid Method Transformer, Word2Vec, Attention and Bi-LSTM Zainuddin, Zahir; Mudassir, -; Tahir, Zulkifli
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2902

Abstract

Named Entity Recognition (NER) is a crucial task in Natural Language Processing (NLP) that identifies entities such as person names, locations, and organizations within the text. While many NER studies have concentrated on the English language, there is a significant need for further research on Indonesian NER. Indonesia presents unique challenges due to its structural complexities, polysemy, and ambiguities. Conventional machine learning and deep learning techniques have been widely applied in NER; however, more detailed exploration into integrating these methods for performance improvement is needed. This study introduces a novel hybrid model, TWBiL, which combines Transformer mechanisms, Word2Vec embeddings, Bidirectional Long Short-Term Memory (Bi-LSTM), and Attention mechanisms to enhance NER performance on Indonesian text. TWBiL harnesses the strengths of each component to generate superior word vector representations, extract intricate sentence features, and disambiguate entities contextually. Our experimental results demonstrate the effectiveness of the proposed hybrid model, revealing a significant improvement in NER performance. Specifically, TWBiL achieves an F1-Score of 85.11 on an Indonesian online news dataset, outperforming the traditional Bi-LSTM model, which achieved a score of 75.18. The results indicate that TWBiL effectively reduces ambiguity and captures context more accurately, enhancing entity recognition. Future research should priorities reducing computational time when handling larger datasets without compromising overall NER performance. This study underscores the potential of integrating advanced deep learning techniques to tackle the unique challenges of Indonesian NER, thus providing a solid foundation for further advancements in the field.
Co-Authors ', Wardi A.Ais Prayogi Abdul Hafid Abdul Halik Lateko Abdul Latief Arda Adnan - Adnan Adnan Adnan Adnan ADRIANI ADRIANI Ady Wahyudi P Ady Wahyudi Paundu Ahmad Abdullah Ahmad Agun Ahyar, Muh. ALAUDDIN Y, ALAUDDIN Amil Ahmad Ilham Aminuddin Aminuddin Andani Achmad Andani Achmad Andi Abd Halik Lateko Ti Andi Alviadi Nur Risal Andi Nilawati Usman Ansar Suyuti Anugrayani Bustamin Arbi, M. Kurniawan Areni, Intan Sari Arfandi, Ilham Arthanugraha, Wahyu Arya Samman, Faizal Asdi Asep Indra Syahyadi Aswad, Iqra Aulia Darnilasari Basir, Badirun Bunga, Wahyuddin Burhanuddin Bahar Christoforus Yohanes darman, muh. yusril Duyo, Risal A Duyo, Rizal Duyo, Rizal Ahdiyat Elly Warni Farid Wajidi Fauzan, Arief Firgiawan, Wawan Haliah, Haliah Hasanuddin, ZulfajriBasri Hasnawati Hasnawati Hasnawiya Hasan Hasnawiyah Hasan Husain, Muhammad Fadhil Ikram Saopna Indrabayu Indrabayu Ingrid Nurtanio Intan Sari Areni Iqra Aswad Janwar Abbas Kelsaba Kahpi, Ashabul kurniawan, nawan Lateko, Andi Abd Halik Marindah Wardhani, Tyanita Puti Mawardi, Fuad Aliefah Al Mohammad Fajar, Mohammad Mudassir, - Muh. Alief Fahdal Imran Oemar Muhammad Niswar Mukarramah Yusuf Mukhtar, Aswar Mukhtar Nirwana, Hafsah Oemar, Imran Pangerang, Fitriaty Paundu, Ady Wahyudi Quraisy, Muh Imam Raden Wirawan Rhiza S. Sadjad Rieka Zalzabillah Putri Saputri Laswi, Aishiyah Sudirman Sudirman Sundun, Ririn Rezky Ananda Supriadi Sahibu Syafruddin Syarif Syahrul R, Syahrul Umasugi, Afriansyah Wardi Wardi Wardihan, Fatmawati Zaenab - Zaenab Muslimin Zakati, Zayyid Zakki Mubarak, Zakki Zulfajri B. Hasanuddin Zulkifli Tahir