Question Answering Berbasis Transformer pada Koleksi Hadis Kutubut Tis’ah untuk Pembelajaran Pendidikan Agama Islam
Main Article Content
Abstract
Perkembangan teknologi Natural Language Processing (NLP) berbasis Transformer telah mendorong inovasi dalam sistem Question Answering (QA). Dalam konteks pembelajaran hadis di Pendidikan Agama Islam, akses terhadap koleksi Kutubut Tis’ah masih menghadapi kendala dalam pencarian informasi yang cepat dan kontekstual. Oleh karena itu, diperlukan sistem cerdas yang mampu memahami pertanyaan pengguna dan memberikan jawaban yang relevan secara otomatis. Penelitian ini mengembangkan sistem Question Answering berbasis Transformer dengan memanfaatkan model pra-latih BERT (Bidirectional Encoder Representations from Transformers). Tahapan penelitian meliputi pengumpulan dan praproses data hadis Kutubut Tis’ah, anotasi dataset, perancangan arsitektur model, pelatihan, serta pengujian sistem. Dataset dibagi menjadi data latih dan data uji dengan proporsi 80:20. Evaluasi dilakukan menggunakan metrik Exact Match (EM) dan F1-score untuk mengukur performa model. Hasil pengujian menunjukkan bahwa model yang dikembangkan memperoleh nilai Exact Match (EM) sebesar 78,6% dan F1-score sebesar 86,3%. Sistem mampu memberikan jawaban yang relevan dan kontekstual terhadap berbagai variasi pertanyaan pengguna, serta berhasil mengidentifikasi bagian teks hadis yang sesuai sebagai jawaban dengan tingkat akurasi yang baik. Penerapan model Transformer terbukti efektif dalam meningkatkan kualitas pencarian informasi hadis dibandingkan metode konvensional berbasis pencocokan kata kunci. Sistem ini berpotensi menjadi media pembelajaran hadis yang interaktif, efisien, dan mudah diakses.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
License Terms
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
References
Abdallah, A., Piryani, B., & Jatowt, A. (2023). Exploring the state of the art in legal QA systems. Journal of Big Data. https://doi.org/10.1186/s40537-023-00802-8
Afabih, A., Roziqi, A., & Maulana, A. (2025). THE IMPACT OF SCHOLAR ’ S DIFFERENCES IN DETERMINING THE STATUS OF HADITH ON THE LAW. 6(1), 125–152. https://doi.org/10.55987/njhs.v6i1.219
Ahmed, M., Khan, H., Iqbal, T., & Alarfaj, F. K. (2023). On solving textual ambiguities and semantic vagueness in MRC based question answering using generative pre-trained transformers. 1–31. https://doi.org/10.7717/peerj-cs.1422
Alsubhi, K., Jamal, A., & Alhothali, A. (2022). Deep learning-based approach for Arabic open domain question answering. https://doi.org/10.7717/peerj-cs.952
Ananda, F., Rakha, G., Ardiansyah, R., & Syirajuddin, M. (2025). Design and Development of a Soccer Shoes Recommendation Application Using NLP Model Implementation and Content-Based Filtering. 15(3), 300–307. https://jurnal.polinema.ac.id/index.php/jartel/article/view/6965
Kim, Y., Bang, S., Sohn, J., & Kim, H. (2022). Question answering method for infrastructure damage information retrieval from textual data using bidirectional encoder representations from transformers. Automation in Construction, 134(October 2021), 104061. https://doi.org/10.1016/j.autcon.2021.104061
Maxutova, K. (2025). Development of a Hybrid Span-QA Model With Ontology Integration for Semantic Enrichment of Answers. August. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=11159216
Mitchell, J. R., Szepietowski, P., Howard, R., Reisman, P., Jones, D., Lewis, P., Fridley, B. L., & Rollison, D. E. (n.d.). A Question-and-Answer System to Extract Data From Free-Text Oncological Pathology Reports ( CancerBERT Network ): Development Study Corresponding Author : 24. https://doi.org/10.2196/27210
Ni’mah, A. T., & Yunitarini, R. (2024). Relevance of the Retrieval of Hadith Information (RoHI) using Bidirectional Encoder Representations from Transformers (BERT) in religious education media. BIO Web of Conferences, 146. https://doi.org/10.1051/bioconf/202414601041
Sartika, D., S, M. N., Putri, S., & Mulyani, R. (2025). Prevention Strategies for Cyberbullying Based on Hadiths : A Thematic Analysis of Hadiths Pertaining to Ethical Communication. 49(2), 165–182. https://doi.org/10.24014/an-nida.v49i2.38192
Shao, T. (2019). Collaborative Learning for Answer Selection in Question Answering. 7, 7337–7347. https://ieeexplore.ieee.org/abstract/document/8648373
Shen, K., & Id, M. K. (2023). Quantifying confidence shifts in a BERT-based question answering system evaluated on perturbed instances. 1–21. https://doi.org/10.1371/journal.pone.0295925
Souza, F. C., Nogueira, R. F., & Lotufo, R. A. (2023). BERT models for Brazilian Portuguese: Pretraining, evaluation and tokenization analysis. Applied Soft Computing, 149(PA), 110901. https://doi.org/10.1016/j.asoc.2023.110901
Tsalitsatun, A., Yahya, A., Winata, S., Ramansyah, W., Nawawi, S., Yuanova, R. A., Mansur, A. M., & Muhammed, A. (2025). Implementation of a transformer-based question answering model in KutubBot for the Kutubut Tis ’ ah Hadith Corpus. 01036. https://www.epj-conferences.org/articles/epjconf/abs/2025/29/epjconf_aiptec2025_01036/epjconf_aiptec2025_01036.html
Tsalitsatun, A., Yunitarini, R., & Nawawi, S. (2024). Pembelajaran Pendidikan Agama Islam melalui Sistem Pencarian Artikel Berbasis Bidirectional Encoder Representations from Transformers ( BERT ). 4(2), 187–195. https://nuris.ac.id/journal/jeis/article/view/181
Wang, C., Zhang, L., & Yan, W. (2023). Relation Extraction Based on BERT and BGRU in the Chinese Music Scene. Procedia Computer Science, 225, 2429–2438. https://doi.org/10.1016/j.procs.2023.10.234
Yang, J., Yang, X., Li, R., Luo, M., Jiang, S., Zhang, Y., & Wang, D. (2023). BERT and hierarchical cross attention-based question answering over bridge inspection knowledge graph. Expert Systems with Applications, 233(June), 120896. https://doi.org/10.1016/j.eswa.2023.120896
Zhu, X., Chen, Y., Gu, Y., & Xiao, Z. (2022). SentiMedQAer : A Transfer Model for Biomedical Question Answering. 16(March), 1–11. https://doi.org/10.3389/fnbot.2022.773329