<![CDATA[Customer service in Micro, Small, and Medium Enterprises (MSMEs) is generally still conducted manually, resulting in delayed responses, inconsistent information, and high operational workload. This study aims to develop and evaluate a customer service chatbot based on Large Language Model using the Retrieval-Augmented Generation (RAG) method as an automation solution for customer service at KampusMadu MSME. The system was developed using an adaptive Waterfall approach encompassing requirements analysis, design, implementation, and testing stages. Testing was conducted through three approaches: functional testing using Black Box Testing method, retrieval accuracy testing using Precision@3, Recall@3, and Hit Rate@3 metrics, and usability testing using the System Usability Scale (SUS) involving 10 respondents. The test results show that all system functions operate as required, with average Precision@3 of 63.3%, Recall@3 of 72.5%, and Hit Rate@3 of 100%. The SUS score obtained was 69.75 (category "OK"), indicating that the system is reasonably acceptable to users. The chatbot system was successfully integrated into the KampusMadu website as an interactive widget directly accessible to customers. This study demonstrates that the application of RAG in MSME chatbots can effectively support the automation of customer service in a more responsive, informative, and contextual manner.]]>
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