Dice Similarity and TF-IDF for New Student Admissions Chatbot

Abstract


 
 
 CS is one of the most important functions of any client-related organization, whether a business or a school (customer service). Notably from the committee responsible for student selection, CS, on the other hand, has a very limited capacity to be handled by humans, which can reduce university satisfaction. Therefore, we require technological assistance, which in this case takes the form of an AI-based chatbot. The objective of this study is to design and develop a chatbot system utilizing NLP (natural language processing) to aid the CS of the new student admissions committee at Pahlawan Tuanku Tambusai University in answering questions from prospective new students. The employed method is dice similarity weighted by TFIDF. The results of the conducted tests indicated that the recall rate was 100 percent and the precision reached 76.92 percent. The evaluation results indicate that the chatbot can effectively respond to questions from prospective students.