Chatbot-Based Library Service Optimazation: Implementing Levenshtein Distance A Case Study at Esa Unggul University

Library information services at Esa Unggul Citra Raya University are still manual, requiring students to come directly during operational hours to get information. This condition causes long queues, long waiting times, and limited access to information outside of operational hours. This research aims to develop a fuzzy matching-based chatbot application to improve the efficiency of library information services. The development method uses Extreme Programming (XP) with stages of planning, design, coding, and testing. The system is built using Python with the Flask framework as the backend, MySQL database for data storage, and the Levenshtein Distance algorithm for fuzzy matching that is capable of handling typos. Data collection was carried out through observations, interviews with library staff, and questionnaires to 168 students. Testing using the Blackbox Testing method shows that all system functions are running according to specifications. Performance evaluation through questionnaires to 53 respondents showed positive results with 81.1% of respondents stating that the chatbot was able to understand questions despite typing errors, 88.7% rated the answers as very appropriate, 90.6% stated that the response was very fast, and 86.8% felt very satisfied. The system is proven to save 1.17-2.33 hours of staff work time per day, reduce queues at staff desks, and provide 24/7 access to information. The implementation of the chatbot successfully improves the efficiency of library services and provides an optimal user experience.

Keywords: Chatbot, Fuzzy Matching, Flask, Library Services, Levenshtein Distance