Application of Natural Language Processing and LSTM in A Travel Chatbot for Medan City
DOI:
https://doi.org/10.31961/eltikom.v9i1.1481Keywords:
Travel Chatbot, Artificial Intelligence, Natural Language Processing, Long Short Term MemoryAbstract
The tourism sector plays a vital role in economic growth and regional development. Medan, a major city in North Sumatra, offers rich religious, historical, and cultural attractions. However, fragmented and inconsistent information presents challenges for both tourists and destination managers, often complicating travel planning. To address this issue, this study proposes the development of an AI-based chatbot aimed at enhancing the tourism experience in Medan. By integrating Natural Language Processing (NLP) and Long Short-Term Memory (LSTM), the chatbot is designed to deliver accurate, contextual, and conversational responses tailored to users' tourism-related queries. It was trained on a comprehensive dataset compiled from various sources concerning Medan’s tourism. The training ran over 100 epochs, achieving an accuracy of 84.31% and a loss of 0.7594. Validation testing yielded an accuracy of 77.14% and a loss of 2.4233, indicating good generalization to unseen data. End-to-end testing with 312 queries covering all defined intents resulted in a testing accuracy of 75.64%, confirming the model’s practical effectiveness. The findings demonstrate that the chatbot can accurately interpret user input, classify information, and enhance user interaction. supports the digital transformation of Medan’s tourism sector by introducing a reliable, AI-driven tool for seamless travel planning and engagement.
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