Implementation of IndoBERT-Based Chatbot for Information and Marketing Services on the Griya Alam Mandiri Website

Abstract

Property information services at Griya Alam Mandiri currently rely on manual WhatsApp interactions during working hours, often leading to delayed responses for prospective buyers. This study aims to implement a Natural Language Processing (NLP) based chatbot using the IndoBERT model on the housing website to automate 24/7 customer service. The methodology involves extracting 3,082 training data samples augmented with typos and informal language, model fine-tuning, and benchmarking against comparative architectures. The evaluation results show that the IndoBERT Base model highly excels in intent classification with an accuracy of 95.85% and an F1-Score of 95.87%, outperforming Indonesian RoBERTa, IndoBERTweet, and XLM-RoBERTa. This system was successfully integrated into a Client-Server architecture connecting a React interface and a FastAPI backend. The final evaluation through User Acceptance Testing (UAT) involving 50 respondents achieved an overall feasibility percentage of 82.50% (Very Satisfied). In conclusion, the IndoBERT chatbot is proven effective in improving the speed of real-time information services and successfully reduces administrative workloads. Future research suggestions include integrating Large Language Models (LLM) with the Retrieval-Augmented Generation (RAG) method to generate more dynamic responses.