<![CDATA[Digital transformation has encouraged public sector organizations to enhance the quality and efficiency of public service delivery. One strategic service that plays a critical role in improving public governance is public complaint management. However, the high volume and diverse content of incoming complaints often require manual classification and routing, which may lead to processing delays, inconsistent categorization, and increased administrative workload. This study aims to analyze the application of Artificial Intelligence (AI) based on Natural Language Processing (NLP) to improve the operational efficiency of digital public complaint services from a business and managerial perspective. The research adopts the CRISP-DM framework, with particular emphasis on the business understanding and implementation phases. The dataset consists of textual public complaints analyzed using machine learning–based classification models. The findings indicate that the implementation of AI can accelerate the complaint classification process, improve decision consistency, and support operational efficiency in public service delivery. This study provides practical insights for public organizations seeking to adopt AI as a decision-support tool while maintaining the essential role of human oversight.]]>
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