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This study aims to develop an Islamic Question Answering (QA) system based on RESTful Web Services. The QA system is designed to provide accurate and relevant answers to questions within the domain of Islamic studies using textual resources such as the Qur'an, Hadith, and other religious literature as its knowledge base. The design phase involves the development of a database schema and REST API endpoints, while the implementation phase employs Django and the Django REST Framework (DRF) to integrate the QA model with the REST API. System testing was conducted using the Postman application to ensure that all endpoints functioned as expected. The results of the study indicate that the Islamic QA system is not only flexible and easy to implement but also capable of integration with various other platforms.
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Referensi
- Ahmed, B. H., Saad, M. K., & Refaee, E. A. (2022). QQATeam at Qurán QA 2022: Fine-Tunning Arabic QA Models for Qurán QA Task. 5th Workshop Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur’an QA and Fine-Grained Hate Speech Detection, OSACT 2022 - Proceedings at Language Resources and Evaluation Conference, LREC 2022.
- Bae, S., Kim, D., Kim, J., & Choi, E. (2021). Question Answering for Complex Electronic Health Records Database using Unified Encoder-Decoder Architecture. Proceedings of Machine Learning Research, 158.
- Baker, M. (2022). Secure Web Application Development: A Hands-On Guide with Python and Django. In Secure Web Application Development: a Hands-On Guide with Python and Django. https://doi.org/10.1007/978-1-4842-8596-1
- C Hillar, G. (2018). Django Restful Web Services The Easiest Way to Build Python Restful Api and Web Services With Django. In Django RESTful Web Services.
- Fielding, R. T., & Taylor, R. N. (2002). Principled Design of the Modern Web Architecture. ACM Transactions on Internet Technology, 2(2). https://doi.org/10.1145/514183.514185
- Hamed, S. K., & Aziz, M. J. A. (2016). A question answering system on Holy Quran translation based on question expansion technique and Neural Network classification. Journal of Computer Science, 12(3). https://doi.org/10.3844/jcssp.2016.169.177
- Lende, S. P., & Raghuwanshi, M. M. (2016). Question answering system on education acts using NLP techniques. IEEE WCTFTR 2016 - Proceedings of 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare. https://doi.org/10.1109/STARTUP.2016.7583963
- Momtazi, S., & Abbasiantaeb, Z. (2022). Question Answering over Text and Knowledge Base. In Question Answering over Text and Knowledge Base. https://doi.org/10.1007/978-3-031-16552-8
- Nurjayanti, Adiwijaya, & Al Faraby, S. (2022). LEARNING CONTEXTUAL MEANING FOR QUESTION RETRIEVAL USING SIAMESE LSTM ON ISLAMIC QUESTION ANSWERING SYSTEM. ICIC Express Letters, 16(9). https://doi.org/10.24507/icicel.16.09.1011
- Thomas, A., & Sangeetha, S. (2022). Knowledge Graph Based Question-Answering System for Effective Case Law Analysis. Smart Innovation, Systems and Technologies, 267. https://doi.org/10.1007/978-981-16-6616-2_27
Referensi
Ahmed, B. H., Saad, M. K., & Refaee, E. A. (2022). QQATeam at Qurán QA 2022: Fine-Tunning Arabic QA Models for Qurán QA Task. 5th Workshop Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur’an QA and Fine-Grained Hate Speech Detection, OSACT 2022 - Proceedings at Language Resources and Evaluation Conference, LREC 2022.
Bae, S., Kim, D., Kim, J., & Choi, E. (2021). Question Answering for Complex Electronic Health Records Database using Unified Encoder-Decoder Architecture. Proceedings of Machine Learning Research, 158.
Baker, M. (2022). Secure Web Application Development: A Hands-On Guide with Python and Django. In Secure Web Application Development: a Hands-On Guide with Python and Django. https://doi.org/10.1007/978-1-4842-8596-1
C Hillar, G. (2018). Django Restful Web Services The Easiest Way to Build Python Restful Api and Web Services With Django. In Django RESTful Web Services.
Fielding, R. T., & Taylor, R. N. (2002). Principled Design of the Modern Web Architecture. ACM Transactions on Internet Technology, 2(2). https://doi.org/10.1145/514183.514185
Hamed, S. K., & Aziz, M. J. A. (2016). A question answering system on Holy Quran translation based on question expansion technique and Neural Network classification. Journal of Computer Science, 12(3). https://doi.org/10.3844/jcssp.2016.169.177
Lende, S. P., & Raghuwanshi, M. M. (2016). Question answering system on education acts using NLP techniques. IEEE WCTFTR 2016 - Proceedings of 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare. https://doi.org/10.1109/STARTUP.2016.7583963
Momtazi, S., & Abbasiantaeb, Z. (2022). Question Answering over Text and Knowledge Base. In Question Answering over Text and Knowledge Base. https://doi.org/10.1007/978-3-031-16552-8
Nurjayanti, Adiwijaya, & Al Faraby, S. (2022). LEARNING CONTEXTUAL MEANING FOR QUESTION RETRIEVAL USING SIAMESE LSTM ON ISLAMIC QUESTION ANSWERING SYSTEM. ICIC Express Letters, 16(9). https://doi.org/10.24507/icicel.16.09.1011
Thomas, A., & Sangeetha, S. (2022). Knowledge Graph Based Question-Answering System for Effective Case Law Analysis. Smart Innovation, Systems and Technologies, 267. https://doi.org/10.1007/978-981-16-6616-2_27