Comparison of Similarity Methods on New Student Admission Chatbots Using Retrieval-Based Concepts

Abstract

A college's students are an essential component. The college always opens registration for new students each year. Every year, more than 1,000 prospective new students register. Because of this, the new student admissions committee is constantly overwhelmed when responding to campus-related questions. As a result, developing a chatbot to assist new students is necessary. The best similarity method is needed for the development of a chatbot using a retrieval-model approach. The New Student Admission Chatbot and the Similarity Method are compared in this study using the Retrieval-Based Concept. The cosine, Jaccard, dice, euclidean, Manhattan, Canberra, and Chebyshev similarity methods are compared. In the context of Universitas Pahlawan Tuanku Tambusai, the data used are information about new students as well as accreditation for study program. There are 41 pieces of information used. Labels and information make up data. According to the test results, the dice and cosine similarity methods are the most effective. On all tested thresholds, dice and cosine similarity achieved an f1-score above 80%. Recall produces extremely optimal results, including 100%.Over 75% of the time, good results are reliably achieved. This demonstrates that the retrieval-model concept can be applied