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The Utilization of Machine Learning on Studying Hadith in Islam: A Systematic Literature Review

Authors :
Bambang Sulistio
Arief Ramadhan
Edi Abdurachman
Muhammad Zarlis
Agung Trisetyarso
Source :
Education and Information Technologies. 2024 29(5):5381-5419.
Publication Year :
2024

Abstract

Computer science development, especially machine learning, is a thriving innovation essential for education. It makes the process of teaching and learning more accessible and manageable and also promotes equality. The positive influence of machine learning can also be felt in Islamic studies, particularly in Hadith studies. This literature review highlights the role of machine learning in managing research regarding Hadith studies that have been published and categorizing it by their research topics, language & corpus, and the machine-learning algorithms. This article review has been conducted on 48 previously published hadith study journals. Then, we summarize existing trends, including trending topics, common language & corpus, and general algorithms often used in previous hadith-related reviews. This article aims to give new insight to help the broad community of researchers interested in these narrations to create fresh and further research with the uncommon topic, language & corpus, and algorithms. Furthermore, this article is also expected to contribute to academics and practitioners as a guide for conducting future research on the application of computer science in Hadith studies. We conclude that the most frequently discussed topic is Hadith Classification at 33.33%, the most widely used language is Arabic at 43.75%, and the most commonly used algorithm is SVM at 12.5%. In addition, the dataset mainly used is a public dataset by Al-Bukhari at 30.53%.

Details

Language :
English
ISSN :
1360-2357 and 1573-7608
Volume :
29
Issue :
5
Database :
ERIC
Journal :
Education and Information Technologies
Publication Type :
Academic Journal
Accession number :
EJ1418766
Document Type :
Journal Articles<br />Information Analyses
Full Text :
https://doi.org/10.1007/s10639-023-12008-9