Implementing a Sharia Chatbot as a Consultation Medium for Questions About Islam

TL;DR

This paper develops a Sharia-compliant chatbot using reinforcement learning and semantic embedding to provide accurate Islamic answers, demonstrating high accuracy and potential for digital religious education.

Contribution

It introduces a novel implementation of a Sharia-compliant chatbot utilizing reinforcement learning and Sentence-Transformers, with a large Islamic QA dataset and a functional mobile interface.

Findings

87% semantic accuracy in testing

Effective across diverse Islamic topics

Potential to enhance religious literacy and digital da'wah

Abstract

This research presents the implementation of a Sharia-compliant chatbot as an interactive medium for consulting Islamic questions, leveraging Reinforcement Learning (Q-Learning) integrated with Sentence-Transformers for semantic embedding to ensure contextual and accurate responses. Utilizing the CRISP-DM methodology, the system processes a curated Islam QA dataset of 25,000 question-answer pairs from authentic sources like the Qur'an, Hadith, and scholarly fatwas, formatted in JSON for flexibility and scalability. The chatbot prototype, developed with a Flask API backend and Flutter-based mobile frontend, achieves 87% semantic accuracy in functional testing across diverse topics including fiqh, aqidah, ibadah, and muamalah, demonstrating its potential to enhance religious literacy, digital da'wah, and access to verified Islamic knowledge in the Industry 4.0 era. While effective for closed-domain queries, limitations such as static learning and dataset dependency highlight opportunities for future enhancements like continuous adaptation and multi-turn conversation support, positioning this innovation as a bridge between traditional Islamic scholarship and modern AI-driven consultation.