<![CDATA[Patient satisfaction is a crucial indicator of hospital quality, yet management often focuses solely on star ratings that fail to explain the root causes of issues. This study develops a hybrid Natural Language Processing (NLP) model using IndoBERT for sentiment classification of Google Maps reviews. Reviews classified as negative sentiment are then filtered and processed using the Latent Dirichlet Allocation (LDA) method to uncover hidden themes within patient complaints. The test results show that the IndoBERT model achieves exceptionally high performance, with an accuracy of 95.23%, precision of 95.22%, recall of 95.23%, and an F1-score of 95.22%. The LDA analysis successfully identifies 10 optimal topics, which are categorized into five main complaint categories: time efficiency, medical services, facilities/parking, administrative procedures, and specialist services. The integration of IndoBERT and LDA proves effective in transforming raw digital reviews into strategic information for the automated evaluation of hospital service quality.]]>
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