## 5 Conclusions  
This study presents significant advancements in ABSA by leveraging the fine-tuned IndoBERT, a pre-trained language model specifically tailored for the Indonesian language. The proposed model effectively captures linguistic nuances, informal expressions, and contextual intricacies in Indonesian student feedback through advanced preprocessing techniques such as normalizing slang words, stemming, and tokenization. These methods enhance the model's ability to extract meaningful insights from the data. Systematic hyperparameter tuning yielded the best configuration with a batch size of 64, a learning rate of 2 \(\times\) 10\({}^{-5}\), 10 epochs, and a dropout rate of 0.4, resulting in an aspect extraction accuracy of 0.973 and an F1-score of 0.952. These results demonstrate the model's superiority over traditional approaches, such as SVM, LSTM, and mBERT. This highlights the effectiveness of language-specific fine-tuning for ABSA tasks in low-resource languages like Indonesian. From a practical perspective, the model enables educational institutions to gain actionable insights into specific aspects of the learning process, such as curriculum design, teaching quality, and infrastructure adequacy. These insights empower data-driven strategies to improve educational quality and enhance student satisfaction. The model's scalability also makes it applicable to other domains, such as customer feedback analysis and public service evaluations, showcasing its broader utility. This research lays the groundwork for future innovations in ABSA for low-resource languages. Expanding the dataset to include diverse feedback across multiple domains could further enhance the model's generalizability. Integrating advanced techniques, such as knowledge graph-based ABSA, may improve the extraction and analysis of complex sentiment relationships. Additionally, developing an interactive, real-time sentiment analysis platform would make this technology more accessible to various stakeholders, fostering widespread adoption across educational and non-educational sectors. In conclusion, the study successfully demonstrates the capability of IndoBERT to address the linguistic and contextual challenges of Indonesian student feedback. By optimizing hyperparameters and incorporating advanced preprocessing techniques, the proposed ABSA model sets a new benchmark for sentiment analysis in low-resource languages, offering precise and actionable insights for enhancing educational quality and beyond. **Author Contributions:** Conceptualization, A.J. ; software, A.J. ; methodology, W. and R.K.; validation, W. and R.K.; writing--review and editing, A.J., W. and R.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Ethical review and approval were waived for this study as the data were obtained through web crawling of publicly available online reviews. No personal or sensitive information was collected, and the study adhered to ethical data usage and privacy protection guidelines. **Informed Consent Statement:** Informed consent was not applicable as the data used in this study were collected through web crawling of publicly available online reviews from Indonesian students. The data did not involve direct interaction with individuals and were anonymized to ensure privacy and ethical compliance. **Data Availability Statement:** The data supporting the reported results were collected through web crawling of publicly available online reviews from Indonesian students. Due to privacy considerations and ethical restrictions, the raw data are not publicly available. However, the corresponding author can make processed data available upon reasonable request. **Acknowledgments:** This research was supported by Muria Kudus University and the Laboratory of Computer Modelling, Faculty of Science and Mathematics, Diponegoro University, Semarang, Indonesia. **Conflicts of Interest:** The authors declare no conflicts of interest.