Many questions and answers about Islamic law are scattered on the internet and have been explained repeatedly by various sites. One solution is presented by the website www.piss-ktb.com, which creates a web-based source of information in the form of Frequently Asked Questions (FAQ). However, web-based FAQs have a weakness: users still have to browse through the available questions one by one according to the questions they want to know the answers to. Browsing through thousands of FAQs is inefficient and exhausting.

Thus, a chatbot system can become a better alternative to the FAQ website. Still, chatbots are difficult to use because most of their conversations are  hard to understand. A single character error will cause the system to misunderstand its meaning. In reality, users expect a chatbot that can understand everyday language. Thus, it is necessary to develop a chatbot system that can understand various common sentence combinations  in everyday language and understand the meaning of words. In addition, it should be able to predict answers automatically to various kinds of questions and requests, even though the initial training data is relatively low. Therefore, this study aims to develop a system that can provide answers automatically based on user commands in natural language using Global Vectors for Word Representations (GloVe), Convolutional Neural Networks (CNN), and Transfer Learning techniques. The result shows that the use of transfer learning and the Nadam optimizer can improve the system’s performance.