## 2 Materials and Methods  
This section outlines the materials and methods used to develop and evaluate the ABSA model based on IndoBERT, providing sufficient details to enable replication and further application of the findings. The development and training of the ABSA model were supported by high-performance computational resources, including NVIDIA Tesla A100 GPUs. These GPUs facilitated the efficient processing of extensive datasets by enabling parallel computations, particularly during matrix operations central to the BERT architecture. The use of such hardware allowed for faster convergence during training and fine-tuning, accommodating the complexity of the IndoBERT-based ABSA model. GPUs enable parallel computation, accelerating data processing, especially in matrix operations central to NLP algorithms like BERT [13]. Optimizing BERT for ABSA requires extensive fine-tuning, which benefits from the computational power of high-performance GPUs like the NVIDIA Tesla V100 or A100. With their high memory capacity, these GPUs allow larger batch sizes and faster convergence during hyperparameter tuning, which is essential for optimizing BERT to capture aspect-specific sentiments in Indonesian student feedback [14]. Data preprocessing, including tokenization and normalization, was also conducted to align the Indonesian language nuances with BERT's tokenization. This preprocessing was crucial in optimizing BERT's compatibility with the Indonesian dataset, enhancing its ability to extract meaningful insights from unstructured text reviews. Hyperparameters such as learning rate, dropout rate, and batch size were carefully tuned during the optimization process, supported by the GPU's computational capacity to experiment with multiple configurations swiftly [15]. For preprocessing the text data, several Python packages specifically designed for natural language processing (NLP) facilitated the transformation of raw data into a clean, structured dataset suitable for sentiment analysis. NLTK (Natural Language Toolkit) performed tokenization, stopword removal, and stemming, which is essential for converting raw text into manageable units. Additionally, spaCy tokenized and lemmatized the text, reducing words to their base forms, thus enhancing the consistency of the data. Pandas facilitated data cleaning and structuring, ensuring the text was organized adequately for further analysis. These preprocessing steps collectively transformed raw student feedback into a structured and clean dataset, ready for sentiment analysis. Regarding hyperparameter tuning, a systematic procedure identified the optimal settings for the BERT model. Grid search initially explored a range of hyperparameter combinations, including learning rate, batch size, and dropout rate. This technique allowed an exhaustive evaluation of various configurations and their impact on the model's performance. In addition to grid search, random search explored hyperparameters like the number of epochs and learning rate, efficiently covering a broader range of values. To ensure the model's robustness and generalizability, k-fold cross-validation was employed for each hyperparameter configuration. This method evaluated the model across different data subsets, reducing potential bias and overfitting. Various learning rate schedules, including linear learning rate warm-up and decay, were tested to optimize the training process and improve model convergence. Finally, early stopping based on validation loss was applied to prevent overfitting halting training when no improvement occurred after several epochs. This systematic approach to hyperparameter tuning enabled the identification of optimal configurations, leading to the best model performance, as reflected in the accuracy and F1-score metrics. In ABSA, aspect detection and sentiment classification are computationally intensive tasks due to the complex word relationships.