Chatbot Berbasis NLP dan Multinomial Naive Bayes untuk Otomatisasi Layanan RJ Visual Stories
Abstract
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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Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Bina Darma, Kota Palembang, Provinsi Sumatera Selatan, Indonesia.
Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Bina Darma, Kota Palembang, Provinsi Sumatera Selatan, Indonesia.
Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Bina Darma, Kota Palembang, Provinsi Sumatera Selatan, Indonesia.
Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Bina Darma, Kota Palembang, Provinsi Sumatera Selatan, Indonesia.
Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Bina Darma, Kota Palembang, Provinsi Sumatera Selatan, Indonesia.
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