<![CDATA[The development of information and communication technology (ICT) provides opportunities for healthcare institutions to improve service quality through the digitisation of patient satisfaction evaluation processes. XYZ Hospital still uses manual methods to measure patient satisfaction, resulting in a slow and error-prone recapitulation process. This study aims to design and implement a sentiment analysis-based patient satisfaction system using the IndoBERT method integrated with quantitative Likert scale measurements based on the SERVQUAL dimensions. The IndoBERT model is used to classify positive and negative sentiments, while the Likert score provides a numerical representation of service quality. The study uses a hybrid approach by processing qualitative data in the form of 2,358 patient text reviews and quantitative data from the SERVQUAL questionnaire, which has been tested for validity and reliability. The IndoBERT model was trained and tested with an 80:20 data split and evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the IndoBERT model is capable of classifying patient satisfaction sentiment with 91.10% accuracy and relatively balanced performance across both sentiment classes. The integration of sentiment analysis results and SERVQUAL scores is presented in an interactive dashboard to support decision-making at XYZ Hospital. This research contributes to the development of a more comprehensive, automated, and data-driven patient satisfaction evaluation system to support improvements in healthcare quality.]]>
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