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Suwanto Sanjaya
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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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.
A web-based decision support system for e-wallet selection using AHP-TOPSIS with integrated economic and technical values Rahmat Zuhri Hafidz; Suwanto Sanjaya; Yelfi Vitriani; Fitra Kurnia; Febi Yanto
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.v6i3.394

Abstract

The rapid growth of financial technology in Indonesia has introduced a diverse range of digital wallet (e-wallet) options including OVO, GoPay, DANA, and ShopeePay. While this abundance of choice benefits consumers, it also creates decision-making challenges, particularly since most prior studies have neglected the balanced integration of economic and technical criteria in e-wallet evaluation. This study addresses that gap by developing a web-based decision support system for e-wallet selection using the AHP-TOPSIS method with integrated economic and technical criteria. Nine criteria were applied, comprising three economic criteria (cost structure, financial incentives, additional fees) and six technical criteria (data security, ease of use, merchant coverage, transaction speed, customer service, additional features). Primary data were collected from 85 active e-wallet users through a Likert scale 1 – 5 questionnaire. Results indicate that OVO ranked first with a TOPSIS preference score of 0.5835, followed by DANA (0.5802), GoPay (0.4654), and ShopeePay (0.3994). The developed system demonstrated the ability to produce objective, adaptive, and user-friendly recommendations to empower users with an interactive, data-driven decision support tool.
Co-Authors Febi Yanto Fitra Kurnia Irpan Afrizal Putra Eriani Muhammad Irsyad Nazruddin Safaat Harahap Rahmat Zuhri Hafidz Yelfi Vitriani