<![CDATA[RJ Visual Stories faces delayed customer service due to manual handling of repetitive inquiries regarding packages, pricing, schedules, and portfolios. This study develops a conversational chatbot using Natural Language Processing (NLP) and the Multinomial Naive Bayes algorithm to provide automated customer support operating continuously (24/7). Using a Research and Development (R&D) method with a prototyping approach, the classification model was trained on 575 conversation samples across 15 intent categories utilizing TF-IDF (unigram-bigram) feature extraction. The model achieved 72.17% accuracy and a 71% weighted F1-score on the test set, with a stable average accuracy of 67.48% under 5-fold stratified cross-validation. Functional evaluation through black-box testing demonstrated a 100% success rate across 20 test scenarios. The developed system successfully automates customer inquiry handling, centralizes business information, and simplifies booking within a single chat interface, effectively eliminating administrative response delays and enhancing real-time customer engagement.]]>
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