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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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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.
Analisis Sentimen Media Sosial X Terhadap Kebijakan Presiden Republik Indonesia Prabowo Subianto Najiyah, Ina; Rizal, Miftahul
JURNAL FASILKOM Vol. 15 No. 3 (2025): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v15i3.10385

Abstract

This study aims to identify and measure the tendency of public sentiment towards the implementation of the policies of the President of the Republic of Indonesia, Prabowo Subianto. The methodology used is text mining-based sentiment analysis, utilizing a data corpus taken from the social media platform X. This study adopts the SEMMA (Sample, Explore, Modify, Model, Assess) workflow as a procedural framework. Data retrieval is carried out automatically using crawling techniques. Next, the data goes through a comprehensive text pre-processing stage, including cleaning, case folding, normalization, convert negation, tokenizing, stopword removal, stemming. Sentiment polarity is determined automatically through a lexicon-based approach, implemented with the VADER (Valence Aware Dictionary for Sentiment Reasoning) algorithm. The modeling phase uses two machine learning classification algorithms, namely Naïve Bayes and Support Vector Machine (SVM). Performance testing is carried out on three different training and testing data distribution schemes (90:10, 80:20, and 70:30). The evaluation findings show that the Naïve Bayes algorithm achieved the highest accuracy rate of 81.25% at a ratio of 80:20. Meanwhile, SVM consistently recorded superior accuracy, reaching a maximum value of 92.60% at a ratio of 90:10. Based on a comprehensive assessment of performance metrics (accuracy, precision, recall, and f1-score), the Support Vector Machine (SVM) algorithm was proven to provide significantly superior performance compared to Naïve Bayes in this sentiment classification task
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