<![CDATA[This research discusses the application of NLP (Natural Language Processing) methods in everyday language use in virtual assistant Chatbots. The issue at hand is the lack of interactivity when communicating with Chatbots, caused by the use of formal language without voice output and a complicated interface. This problem was identified from three respondents through a ChatGPT satisfaction questionnaire. The goal is to meet user needs by making the Chatbot more interactive by adding voice output and an easy-to-use interface. The development method used is the Waterfall Model. In this study, the author developed a Chatbot system using machine learning, specifically the Gradio App as the interface, Python as the programming language, Visual Studio Code as the text editor, MySQL as the database, and the ChatGPT API as the main engine. By applying rules to the Chatbot system, it produced everyday language output and voice output using the TTS (Text to Speech) feature from the ChatGPT API. The research results showed that from a questionnaire of 24 respondents, ChatGPT users who directly tested Chillbot provided positive feedback, with a user satisfaction rate of 87.17%. It is hoped that this system can help advance technology, assist many people in dealing with problems, and better understand user intentions in interactions with Chatbots.]]>
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