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All Journal JOIN (Jurnal Online Informatika) Jurnal Informatika Jurnal Ilmiah Universitas Batanghari Jambi JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Responsif : Riset Sains dan Informatika Jurnal Sosial dan Abdimas Jurnal Ilmiah Teknik Informatika dan Komunikasi Jurnal Teknik Indonesia Ulil Albab SEMINAR TEKNOLOGI MAJALENGKA (STIMA)
Ina Najiyah
Universitas Adhirajasa Reswara Sanjaya
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences Transportation

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Journal : Journal of Applied Data Sciences

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Searching Sahih Hadiths Based on Queries using Neural Models and FastText Susanti, Sari; Najiyah, Ina; Ramdhani, Yudi; Herliana, Asti; Muckti, Masaldi Kharisma; Oktaviani, Fani Rahma
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.467

Abstract

Hadith is the second source of Islamic law after the Qur’an, and the availability of accurate and easily accessible information about hadith is crucial, as it directly affects a person’s belief (aqidah). This highlights the importance of having hadith collections as essential guidance in everyday life. Today, digital versions of hadiths are available in various applications, e-books, and websites. However, users often complain that these sources are incomplete and do not contain the entire collection of the Prophet's hadiths from al-Kutub as-Sittah. Additionally, the complex presentation of these digital resources makes it difficult to find relevant hadiths efficiently. This study aims to improve access to accurate and relevant hadith information, focusing specifically on al-Kutub as-Sittah, using Information Retrieval systems that search for hadiths based on keywords. IR is employed because it has proven effective in retrieving precise documents according to the search terms. A Neural Network is used to match user queries with the document collection, while FastText word embedding is implemented for text representation. FastText is particularly useful for detecting similar meanings across different words, which is essential when interpreting Indonesian-translated hadiths that require nuanced understanding. The dataset used in this study consists of 31,275 Indonesian-translated hadiths from al-Kutub as-Sittah. In this study, it was found that many hadith translations have ancient language so that query reformulation is needed to get the right hadith because users often enter commands with currently trending words. In this study, it was also found that word2vec has less performance than FastText in weighting words in hadith translations. The results indicate that the neural network performs well in retrieving relevant hadith content according to the user’s commands or keywords. With a training data proportion of 70% and a testing data proportion of 30%, the Recall value was 0.7721 and the Precision value was 0.75112.
The Application of Deep Learning in Qur’anic Tafsir Retrieval Using SBERT, FAISS and BERT-QA Herliana, Asti; Najiyah, Ina; Susanti, Sari; Billah, Lutfhi Muayyad
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1000

Abstract

Accurate understanding of the Qur’an requires access to reliable tafsir, yet many classical tafsir resources remain non-digital, making search and retrieval time-consuming. This study presents a semantic-based retrieval system for Tafsir Ibn Kathir, covering 114 entries and 6,236 Verses, using SBERT embeddings and FAISS indexing. The system enables users to perform semantic queries, retrieving relevant passages in response to their questions. Evaluation was conducted using 50 representative queries spanning diverse topics, including Fiqh, Aqidah, History, and Spirituality. Relevance judgments were independently provided by three Qur’anic studies experts and reconciled through discussion, with inter-annotator agreement indicating substantial consistency. Each query included 20 non-relevant passages as negative samples to increase evaluation difficulty. Two approaches were tested: retrieval-only and retrieval combined with a zero-shot QA module for span extraction. Retrieval-only achieved slightly higher top-1 accuracy (0.72), but retrieval + QA improved ranking-oriented metrics, including Accuracy@5 (0.88), Mean Reciprocal Rank (MRR = 0.76), and normalized Discounted Cumulative Gain at 5 (nDCG@5 = 0.82), with the increase in Accuracy@5 statistically significant (p = 0.01). The zero-shot QA module enabled the system to extract more precise and contextually relevant information, enhancing overall retrieval quality and robustness. These results indicate that the proposed system effectively retrieves relevant tafsir passages and provides accurate, context-specific answers. The study demonstrates the potential and limitations of zero-shot QA for domain-specific religious texts and supports the development of web-based applications or Islamic chatbots, facilitating easier access to shahih tafsir knowledge for scholars and the broader Muslim community.
Co-Authors Abillutfi, Ramadhan Muhammad Ade Mubarok Agus Hermawan Ali Akbar Rismayadi Asti Herliana, Asti Billah, Lutfhi Muayyad Handayani, Rissa Hariyanti, Ifani Hikmah Abdillah, Hilman Imam Saepul Azmi Ivan Sofyan Muckti, Masaldi Kharisma Musaropah, Resa Novia Sri Mulyani Oktaviani, Fani Rahma Pramanik, Niskala Dian Ramadhan, Muhammad Azis Rangga Habdulatif Riyand Fibrianto Rizal, Miftahul Salman Topiq Sandini, Dwi Sari Susanti, Sari Suharyanto Suharyanto Wisnuwardana, Dirga Yudi Ramdhani