<![CDATA[This study analyzes user sentiment in Halodoc application reviews using a TextCNN architecture on an Indonesian-language dataset with extreme class imbalance. The dataset consists of 2,000 reviews collected from the Google Play Store and manually labeled into three classes: positive, negative, and neutral. The class distribution comprises 1,800 positive reviews (90%), 175 negative reviews (8.75%), and 25 neutral reviews (1.25%). To reduce bias toward the majority class, the TextCNN model was trained using class weighting and evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The model achieved an accuracy of 93.75% and a weighted F1-score of 0.94, while the macro F1-score was only 0.57. The recall for the neutral class was 0%, indicating that the model was unable to recognize the minority class effectively. The contributions of this study are threefold: an empirical evaluation of TextCNN on Indonesian Halodoc reviews with extreme class imbalance; an analysis of the effect of class distribution using class-level metrics and error analysis; and an identification of the practical implications of the classification results for monitoring digital healthcare service quality. These findings demonstrate that high accuracy alone is insufficient to represent model performance on imbalanced datasets; therefore, strategies such as oversampling, data augmentation, or hybrid approaches should be considered in future research.]]>
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