COBY: COVID-19 Telegram Chatbot by Employing Machine Learning Algorithms

Muhammad Naufaldi, Sunny Jovita; Christensen M. Frans; Muhammad L. I. Hanafi; Nunung N. Qomariyah

COVID-19 pandemic has been one of the biggest concerns nowadays. People always curious and ask for immediate responses regarding the current situation. The chatbot can be very useful in this kind of situation which allows the system to understand text, which means it can respond appropriately. In order to be able to return the correct responses, the chatbot needs to learn how to classify the text data input from the users. In this paper, we study three different machine learning algorithms to work on text classification problems, namely Naive Bayes, Neural Network, and Support Vector Machine (SVM). An experiment was carried out to study which machine learning algorithms produce the most accurate responses when they are implemented in the Artificial Intelligence (AI) chatbot systems. In order to make sure the tests are consistent and fair, we conducted the experiment on the same dataset, and assessed the accuracy of their respective responses. In addition, we have also successfully implemented each of these algorithms as chatbots on a social media platform, Telegram.