<![CDATA[Digital transformation in higher education has generated an increasing volume of textual data, including student comments, academic service evaluations, and feedback on academic information systems. These data contain valuable information for supporting decision-making; however, their unstructured and contextual nature makes manual analysis inefficient. This study aims to compare the performance of a TF-IDF-based Support Vector Machine (SVM) model and a Transformer-based IndoBERT model for sentiment analysis of academic services from student feedback. The dataset consists of 1,700 text entries, combining template-based synthetic data and real-world data collected from social media, which were classified into positive, negative, and neutral sentiment categories. The research process involved exploratory data analysis, text preprocessing, feature extraction, model development, and evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results showed that both models achieved very high performance on the dataset, with an accuracy of 100% on the test set. These findings indicate that both traditional machine learning and Transformer-based approaches are capable of identifying sentiment patterns within the dataset. Nevertheless, the results should be interpreted cautiously, as the relatively homogeneous nature of the dataset and the inclusion of synthetic data may affect the models’ generalizability. The main contribution of this study lies in the comparative evaluation of SVM and IndoBERT within the context of higher education academic services, as well as the development of a sentiment analysis framework that can support data-driven service quality monitoring. Future studies should employ larger, more diverse datasets derived entirely from real-world sources to further validate the findings.]]>
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