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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Abdelaaziz El Hibaoui
Abdelmalek Essaadi University
Computer Science & IT

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Computational methodologies for sanad-based hadith analysis: a review Abdelilah Mhamedi; Mohammed Mghari; Abdelaaziz El Hibaoui
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 3: June 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i3.27447

Abstract

Hadith literature, a cornerstone of Islamic tradition, critically depends on the sanad (chain of narrators) for authentication, a process traditionally requiring profound scholarly expertise. This paper presents a systematic review of computational methodologies designed to enhance and automate sanad analysis, bridging Islamic studies with advanced artificial intelligence (AI). We categorize progress across four key domains: automated authenticity classification, sophisticated narrator network analysis, textual information extraction (e.g., named entity recognition), and the development of specialized datasets and ontologies. Our findings reveal a significant paradigm shift from rule-based systems to advanced machine learning (ML) and deep learning (DL) techniques. This review synthesizes contributions from over 50 studies, highlighting critical challenges including data scarcity, narrator disambiguation, and cross-linguistic resource limitations. We emphasize the novelty of this cross-domain synthesis and discuss how these intelligent systems can be integrated into digital Islamic archives, low-resource mobile hadith applications, and embedded natural language processing (NLP) engines. This work charts a course for future research to develop more robust, scalable, and ethically grounded computational tools, complementing traditional hadith scholarship with advanced engineering solutions
Co-Authors Abdelilah Mhamedi Mohammed Mghari