## 1 Introduction
In recent years, ABSA has been extensively developed using deep learning methods such as convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT) [9]. Among these approaches, the BERT model, with its architecture based on transformer layers, has demonstrated superior performance in comprehending sentence context as a whole and in capturing more complex relationships between words in long sentences. However, although BERT has significantly enhanced ABSA performance in several domains, challenges remain in accurately detecting aspects and overcoming occasional inconsistencies in sentiment classification [7]. Several previous studies have used BERT for ABSA development utilizing public datasets such as SemEval 2014 and SemEval-2016, which consist of English reviews. For instance, some approaches involving post-training techniques before fine-tuning the tar get dataset have proven effective in enhancing ABSA performance [9]. Additionally, approaches using a superficial linear classification layer in the BERT architecture have achieved an F1-score of 61.12, indicating that BERT with this method can outperform traditional models, even without complex hyperparameter adjustments. Another study demonstrated that with certain modifications, a post-trained vanilla BERT model could yield better performance. While these studies have significantly contributed to BERT-based ABSA development on English-language datasets, development for the Indonesian language remains limited. Some studies in Indonesia have applied BERT to hotel and tourist attraction reviews, achieving an F1-score improvement of up to 8% [10]. Most existing studies in ABSA have predominantly used public datasets based on English. At the same time, the development of ABSA models for Indonesian student reviews has been relatively limited, mainly due to the lack of large-scale, publicly available datasets in the Indonesian language. This highlights the need for more in-depth research utilizing specific datasets and more tailored ABSA techniques for the Indonesian language. Therefore, this study aims to address these limitations by using a dataset of student reviews in Indonesian and conducting several experiments, including data preprocessing, hyperparameter fine-tuning, and comparative evaluation of the proposed model against current state-of-the-art models. This approach is expected to yield an ABSA model with more optimal performance and to provide deeper insights into the context of learning evaluation in higher education [11]. Previous studies on ABSA have primarily focused on English datasets, such as SemEval, with limited development in Indonesian contexts, which pose unique linguistic challenges. This study demonstrates the ability of an IndoBERT-based ABSA model to address these challenges, offering actionable insights for higher education institutions. Optimizing the BERT model for Indonesian language data is crucial to maximize its effectiveness in capturing aspect-specific sentiments [12]. This study seeks to refine the application of ABSA by optimizing BERT's hyperparameters and architecture adjustments to handle the complex nuances of Indonesian student feedback. However, although BERT has shown promising results for ABSA in English-based datasets, its application to Indonesian language reviews remains limited and unoptimized [10]. Indonesian student feedback presents unique linguistic challenges due to its mix of cultural expressions, informal language, and complex sentiment structure, which are less prominent in other review types. Optimizing the BERT model for Indonesian ABSA is essential to capture these nuances effectively [9]. This study aims to refine the application of BERT by optimizing hyperparameters and making necessary architectural adjustments, enabling a comprehensive and aspect-specific analysis that provides actionable insights for educational institutions.