Implementation of Chatbot on University Website Using RASA Framework

Abstract

Chatbots are increasingly being utilized to help human performance to boost communication ease and provide better and faster services. A chatbot was built to make it easier to give better and more conveniently available information services 24 hours a day, seven days a week. The suggested chatbot design is constructed using the Rasa framework and is based on the Dual Intent and Entity Transformer (DIET). DIET is a multi-tasking transformer architecture that is both advanced and lightweight. The chatbot will be implemented on the “Politeknik Siber dan Sandi Negara” website, focusing on addressing questions about new student admittance. The chatbot is built with Docker and put as a Chat Widget on the website. The dataset utilized to train the algorithm combines past chat data and data from university social media. The built-in chatbot is evaluated using accuracy metrics and F1 scores. Model evaluation metrics and functionality tests are used in the evaluation. Testing with evaluation results in a precision value of 0.99. a recall value of 1.0, and an F1 score of 0.99. While the functioning test percentage is 90.625 %.