Tinjauan Pengesanan Hadis Palsu Menggunakan Pendekatan Pembelajaran Mesin

  • Borhan Ab Rahman Borhan bin Ab Rahman
  • Dr. Mohd Zakree bin Ahmad Nazri
  • Dr. Latifah binti Abdul Majid
DOI: https://doi.org/10.53840/hadis.v14i28.260
Keywords: Hadis Palsu; Pembelajaran Mesin; Pengesanan Hadis Palsu

Abstract

Hadis Rasulullah SAW merupakan sumber rujukan kedua dalam agama Islam selepas kitab suci Al-Quran. Segala yang dinyatakan dalam hadis Rasulullah SAW menjadi rujukan bagi semua umat Islam di seluruh dunia. Namun, bukan semua yang dinyatakan sebagai hadis ialah sahih bersumberkan Rasulullah SAW kerana terdapat pendustaan yang dinamakan sebagai hadis palsu. Pengesahan hadis palsu telah menjadi isu yang semakin penting dalam bidang ilmu hadis, terutama dengan penyebarannya yang luas melalui platform digital. Walaupun pengesanan hadis palsu secara manual masih menjadi amalan, pendekatan baru menggunakan pembelajaran mesin mempunyai potensi untuk meningkatkan keberkesanan dan ketepatan dalam proses pengesahan hadis. Pelbagai kajian terkini telah dijalankan untuk meneroka penggunaan kaedah kecerdasan buatan sebagai satu penyelesaian yang inovatif bagi mengatasi masalah ketepatan dan konsistensi dalam pengesahan hadis. Keperluan untuk memahami dan mengesahkan hadis dengan tepat telah mendorong penyelidikan yang mendalam dalam memanfaatkan pendekatan pembelajaran mesin untuk mengesan hadis palsu. Kajian ini memberikan ulasan terperinci dan perbandingan kajian yang lepas daripada aspek teknik pembelajaran mesin, set data dan parameter penilaian yang digunakan untuk menilai potensi model yang dicadangkan. Kajian ini juga membincangkan kesukaran, cabaran yang dihadapi dan cadangan yang boleh memanfaatkan potensi pembelajaran mesin dalam domain ilmu hadis.

Downloads

Download data is not yet available.

Author Biographies

Borhan Ab Rahman, Borhan bin Ab Rahman

Currently, Borhan is a PhD student at Universiti Kebangsaan Malaysia, specialising in artificial intelligence. He holds a bachelor's degree in computer science (MIS) from the University of Malaya and a master's degree in computer science (software engineering) from Universiti Putra Malaysia. His research focuses on detecting false information in texts related to Hadith. He is dedicated to developing machine-learning algorithms to enhance the accuracy and effectiveness of these detection systems. His primary research interests include developing methods to analyze Malay texts and addressing challenges in processing unstructured data. 

Dr. Mohd Zakree bin Ahmad Nazri

Mohd Zakree obtained his MSc and Phd from Universiti Teknologi Malaysia in the field of Intelligent Decision Support (MSc) and Machine Learning (PhD) by developing clustering algorithms based on algorithms inspired by nature and biology such as Auto-Immune Systems and Artificial Neural Networks. He teaches the Business Intelligence and Analytics course at the undergraduate level and the Decision Support System course at the undergraduate level. Active in teaching and learning related to data science. The area of research he is currently focusing on is the development of algorithms and methods for unstructured data processing, especially in Malay and Rojak languages.

Dr. Latifah binti Abdul Majid

Latifah Abdul Majid(Majid, L.A) is currently an Associate Professor at the Research Centre for al-Quran and al-Sunnah (PQS), Faculty of Islamic Studies, Universiti Kebangsaan Malaysia. She obtained her master's degree in Islamic Studies (al-Quran and al-Sunnah) from UKM with a dissertation titled: "The Ijtihad of Prophet S.A.W" in 1997. She obtained a doctorate in Religious and Theology (Hadith) from the University of Wales, Lampeter, United Kingdom with a thesis titled: ` The Hadiths as a Tools for Religious, Political and Social Teaching in the Malay Archipelago` in 2008. Currently, her research interests are in: Hadith studies in the Malay archipelago, Hadith Literature and Its Sciences, Orientalism, Liberalism, and Contemporary Issues ( educational adolescents, girls and adolescent women, international women`s concerns, religion spirituality and reproductive health)

References

Abdelaal, H. M. & Youness, H. A. (2019). Hadith Classification using Machine Learning Techniques According to its Reliability. Romanian Journal of Information Science and Technology 22(3–4): 259–271.

Belle, V. & Papantonis, I. (2021). Principles and Practice of Explainable Machine Learning. Frontiers in Big Data 4(July): 1–25. doi:10.3389/fdata.2021.688969

Binbeshr, F., Kamsin, A. & Mohammed, M. (2021). A Systematic Review on Hadith Authentication and Classification Methods. ACM Transactions on Asian and Low-Resource Language Information Processing 20(2). doi:10.1145/3434236

Fadele, A. A., Kamsin, A., Ahmad, K. & Hamid, H. (2021). A novel classification to categorise original hadith detection techniques. International Journal of Information Technology. doi:10.1007/s41870-021-00649-3

Gaanoun, K. & Alsuhaibani, M. (2022). Fabricated Hadith Detection: A Novel Matn-Based Approach With Transformer Language Models. IEEE Access 10(October): 113330–113342. doi:10.1109/access.2022.3217457

Hakak, S., Kamsin, A., Zada Khan, W., Zakari, A., Imran, M., bin Ahmad, K. & Amin Gilkar, G. (2022). Digital Hadith authentication: Recent advances, open challenges, and future directions. Transactions on Emerging Telecommunications Technologies 33(6): 1–14. doi:10.1002/ett.3977

Marozzo, F., Orsino, A., Talia, D. & Trunfio, P. (2022). Edge Computing Solutions for Distributed Machine Learning. Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022 1–8. doi:10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927824

Mohamed, E. & Sarwar, R. (2021). Linguistic features evaluation for hadith authenticity through automatic machine learning. Digital Scholarship in the Humanities. doi:10.1093/llc/fqab092

Najeeb, M. M. A. (2021). Towards a Deep Leaning-based Approach for Hadith Classification. European Journal of Engineering and Technology Research 6(3): 9–15. doi:10.24018/ejers.2021.6.3.2378
Published
2024-12-30
How to Cite
Ab Rahman, B., Ahmad Nazri, M. Z., & Abdul Majid, L. A. M. (2024). Tinjauan Pengesanan Hadis Palsu Menggunakan Pendekatan Pembelajaran Mesin. HADIS, 14(28), 1-11. https://doi.org/10.53840/hadis.v14i28.260
Issue
Vol. 14 No. 28 (2024): HADIS
Section
Bahasa Melayu

Copyright (c) 2024 HADIS

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Upon acceptance of an article, Authors will be asked to transfer copyright. This transfer will ensure the widest possible dissemination of information. A notification will be sent to the corresponding Author confirming receipt of the manuscript.

If excerpts from other copyrighted works are included, the Author(s) must obtain written permission from the copyright owners and credit the source(s) in the article.

Also, this work is licenced under a http://creativecommons.org/licenses/by/4.0