<![CDATA[The use of digital technology in healthcare communication remains minimal in Indonesia, with over 80% of health facilities not yet optimally connected to digital systems. This study aims to design and implement a Natural Language Processing (NLP)-based chatbot integrated with WhatsApp to enhance patient service efficiency at Klinik Khitan Dr. Sarwoko. The system was built using Node.js (Baileys library) for WhatsApp integration and Python (Flask) as the NLP processing backend, with SQLite as the database and an HTML/JavaScript admin dashboard. The system supports over 35 intent categories using a rule-based keyword matching approach with Sastrawi preprocessing. Black Box Testing on 47 test scenarios across 7 categories achieved 100% success rate. White Box Testing on 5 main modules produced an average cyclomatic complexity of V(G) = 4.0 (Low Risk). A questionnaire survey of 20 respondents yielded an overall satisfaction score of 4.42/5 (Excellent). The chatbot operates 24 hours a day with an average response time of 1.5 seconds, significantly reducing repetitive workload for administrative staff and improving patient service accessibility.]]>
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