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Muhammad Irsyad
Department of Informatics Engineering, UIN Sultan Syarif Kasim Riau, Pekanbaru 28293, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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A classification of Quran translations using K-nearest neighbors, support vector machine and random forest method Nur Delifah; Nazruddin Safaat Harahap; Surya Agustian; Muhammad Irsyad; Iwan Iskandar
Science, Technology, and Communication Journal Vol. 6 No. 1 (2025): SINTECHCOM Journal (October 2025)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i1.337

Abstract

A Classification of Quranic verses based on topics is one of the efforts to facilitate understanding and searching for information in the holy book, especially for non-Arabic readers. This study aims to test and compare the performance of three text classification methods, namely K-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF), in grouping translated Quranic verses into 15 topic classes, such as Islamic arkanul, faith, the Quran, science and its branches, charity, da'wah, jihad, human and social relations, and others. The dataset used is the English translation of the Quran with full preprocessing and an 80:20 data split for training and testing. The evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results show that RF achieved the best performance with an average F1-score of 58.48% and testing accuracy of 90.81%. KNN followed with an F1-score of 54.07% and the highest testing accuracy of 92.05%, while SVM produced the lowest F1-score at 50.76% and accuracy of 88.20%. The RF demonstrates a more balanced ability in recognizing all classes, KNN excels in overall accuracy, and SVM performs less optimally in this classification task. This research is expected to serve as a foundation for developing a more intelligent and contextual topic-based verse classification system.
Implementation of the mawaris fiqh hybrid chatbot based on retrieval-augmented generation and rule-based expert system Irpan Afrizal Putra Eriani; Nazruddin Safaat Harahap; Suwanto Sanjaya; Muhammad Irsyad
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.392

Abstract

Islamic inheritance law (mawaris fiqh) regulates the distribution of inheritance based on the Quran, Sunnah, and ijma’. However, many people still have difficulty in understanding the concept of inheritance and performing accurate inheritance calculations due to the complexity of faraidh rules and limited sources of information about faraidh. This study aims to develop a hybrid-based mawaris chatbot that integrates retrieval-augmented generation (RAG) and rule-based expert system to support both conceptual question answering and deterministic inheritance calculations. This system is implemented using the Voyage-3-Large embedding model, Qdrant vector database, semantic caching, large language models (LLM) for contextual response generation using models from GPT-4o (main) and llama3.2:3b (fallback mode) as well as semantic cache using paraphrase-multilingual-MiniLM-L12-v2. The "Ask Concept" answering mode uses semantic search, confidence router, and RAG, while the "Calculate Inheritance" answering mode uses a rule-based expert system for heir identification, validation, faraidh calculation, and division result preparation. The system performance is evaluated for conceptual questions using BERTScore and weighted scoring model (WSM) for inheritance calculation questions. Experimental results show that the conceptual question-answering mode achieves a pass rate of 91.3% on questions in that domain. For inheritance calculation, the RAG-based approach achieves an average score of 44%, while the rule-based expert system achieves 100% in all evaluation categories. These findings indicate that the proposed hybrid architecture effectively combines the contextual reasoning capabilities of RAG with the deterministic accuracy of rule-based calculation, making it a reliable solution for mawaris consultation and inheritance distribution assistance.
Co-Authors Irpan Afrizal Putra Eriani Iwan Iskandar Nazruddin Safaat Harahap Nur Delifah Surya Agustian Suwanto Sanjaya