Penerapan Chatbot pada Aplikasi Web Tanya Jawab Tentang Fiqih Jual Beli Islam Menggunakan LangChain


  • Nurhapiza Nurhapiza UIN Sultan Syarif Kasim, Pekanbaru, Indonesia
  • Nazaruddin Safaat Harahap * Mail UIN Sultan Syarif Kasim, Pekanbaru, Indonesia
  • Muhammad Fikry UIN Sultan Syarif Kasim, Pekanbaru, Indonesia
  • Muhammad Affandes UIN Sultan Syarif Kasim, Pekanbaru, Indonesia
  • (*) Corresponding Author
Keywords: Fiqh Muamalah; LangChain; Large Language Model; OpenAI; Question Answering System

Abstract

Fiqh is the field that studies Islamic rules on how humans behave, both in speech and action. Islamic Fiqh of buying and selling is a branch of fiqh that concentrates on the laws and rules relating to transactions and social interactions that occur in the daily lives of Muslims. There are many sources of learning about the fiqh of buying and selling, including books and the internet. However, manual searches can take a long time and make it difficult for some people to gain in-depth understanding. The application of a chatbot to a question and answer web application can provide a solution to provide easier access. This research aims to provide an effective and efficient solution in understanding fiqh muamalah (Islamic buying and selling). This research develops a question and answer system about the fiqh of Islamic buying and selling to make it easier for users to understand, by utilizing a deep learning approach through technologies such as LangChain, OpenAI, Large Language Model, and ChatGPT 3.5 turbo. Implementation is done through a chatbot web application that provides an initial display and menu, allowing users to ask questions about the fiqh of Islamic buying and selling and see the answers and references. Testing was conducted by students of UIN Sultan Syarif Kasim Riau and an ustaz who has a good understanding of fiqh muamalah using ten questions that were tested through the question and answer web application as a guide. The test results showed an answer evaluation of 88.8% with a very suitable category regarding the correctness of the responses given.

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Submitted: 2024-05-07
Published: 2024-05-30
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