<![CDATA[Hospitals play a crucial role in delivering healthcare services, directly influencing public health and overall well-being. Evaluating service quality is essential for enhancing patient satisfaction, as healthcare institutions must meet patient expectations to ensure effective service delivery. This study assesses hospital service quality using the Servqual model and clustering techniques to analyze patient perceptions. The Servqual framework evaluates five dimensions: tangibility, reliability, responsiveness, assurance, and empathy. A structured questionnaire was used to gather patient responses, utilizing a Likert scale to measure expectations and perceptions. Clustering techniques, including K-Means and Fuzzy C-Means, were applied to segment patient perception data, revealing four service quality categories: "Very Bad" (5%), "Bad" (40%), "Good" (14%), and "Very Good" (40%). Both clustering methods produced identical classifications, highlighting key areas requiring improvement. Among the service attributes assessed, parking area conditions received the lowest ratings, identifying them as a priority for enhancement. To address these issues, recommendations include resurfacing parking areas, implementing clear parking markings, and improving directional signage to enhance accessibility and overall patient experience. This study demonstrates the effectiveness of integrating Servqual with clustering methods in identifying service gaps and prioritizing improvements. By adopting a data-driven approach, hospitals can enhance service delivery, optimize resource allocation, and improve patient satisfaction. The findings provide valuable insights for hospital management and contribute to future research on healthcare service quality enhancement.]]>
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