<![CDATA[The growth of tourist reviews on travel destinations generates textual information that can potentially be used to understand tourists' perceptions, but sentiment classification faces challenges due to class distribution imbalance. This study aims to analyze the performance of IndoBERT in classifying tourist reviews into positive, neutral, and negative categories. The study uses a fine-tuning approach on IndoBERT with 1,500 tourist reviews and evaluates performance using accuracy, precision, recall, F1-score, and a classification report. The test results showed an accuracy of 72.67%, precision of 52.80%, recall of 72.67%, and an F1-score of 61.16%. However, class-wise analysis showed that the model only did well in identifying the neutral class, with a recall of 100% and an F1-score of 84.17%, while the negative and positive classes both scored 0% for precision, recall, and F1-score. This condition is related to the dominance of the neutral class at 73.07% in the dataset. This finding shows that accuracy alone isn't enough to evaluate a model on imbalanced sentiment data, and strategies to handle class imbalance are needed to improve the model's ability to recognize positive and negative sentiments.]]>
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