<![CDATA[Public opinions shared on the Google Maps platform factually reflect the reputation and service quality of higher education institutions in Bali Province. However, manually analyzing thousands of reviews is time-consuming and potentially subjective. Therefore, this research focuses on developing a web-based system equipped with an interactive visual dashboard to computerize the categorization and comparison of review sentiments. The text classification process is built using the IndoBERT (Indonesian Bidirectional Encoder Representations from Transformers) deep learning architecture, which sorts opinions into positive or negative sentiments before presenting them on the web interface. Algorithm performance evaluation shows that IndoBERT is reliable, achieving an accuracy rate of 82.18% and a sensitivity (recall) of 91.90%. The modeling output is visualized dynamically through a dashboard, enabling institutions and prospective students to monitor campus sentiments more efficiently, objectively, and precisely.]]>
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