## 3 Results
### Sentiment Classification
Furthermore, a learning rate of \(2\times 10^{-5}\) ensures stable convergence during the training process. This parameter avoids overfitting, which often occurs at higher learning rates, and prevents underfitting associated with lower learning rates. This combination led to  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Epoch** & **Batch Size** & **Dropout** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline
5 & 15 & 0.2 & 0.850 & 0.837 & 0.851 & 0.851 \\
5 & 15 & 0.4 & 0.876 & 0.866 & 0.872 & 0.859 \\
5 & 15 & 0.6 & 0.864 & 0.861 & 0.860 & 0.84  
an accuracy improvement of 11% compared to the baseline, highlighting its suitability for addressing the linguistic complexities of the Indonesian dataset, including variations in language style and sentence structure. The most effective sentiment classification model was obtained through fine-tuning with hyperparameter settings at epoch 10, batch size 15, and dropout rate 0.4. Based on observations, the epoch value of 10, learning rate 2 \(\times\) 10\({}^{-5}\), and batch size 16 have proven to be effective in developing the most efficient ABSA model. Predefined hyperparameters can perform better aspect detection and sentiment classification than previous studies. This is because the characteristics of student review data differ from data in general, so extra effort is required to test each hyperparameter to obtain the best model performance. This study shows that specific data, such as student reviews, require special treatment in selecting appropriate hyperparameters for optimal results. In this case, aspects such as epoch, batch size, and dropout rate are essential in improving model performance. In addition, the application of pre-training on IndoBERT produces a superior model. Using IndoBERT as a basis, this model can understand a deeper context in student reviews and provide more accurate classification results. This further strengthens IndoBERT's position as a model that can effectively handle the nuances of the Indonesian language, especially in ABSA [12]. This study shows the advantages of selecting the correct hyperparameters and emphasizes the importance of models specifically developed for specific languages and contexts. The superiority of the IndoBERT-based ABSA model provides a new contribution to Indonesian language sentiment analysis, showing that the BERT-based approach can offer better results than previous models in this field [7; 34]. The Confusion Matrix of Model Performance is shown in Figure 6.  
Figure 6 illustrates the correct and incorrect predictions made by the model. For the no sentiment category, represented by the number 483, the model accurately classified 483 instances as having no sentiment. However, the model misclassified one example, which was actually positive, as no sentiment. In the positive category, the model correctly identified 182 instances. Nevertheless, 34 cases that were actually negative were incorrectly predicted as positive. This confusion matrix provides an overview of the model's performance detecting sentiment within the test data. While the model demonstrates high accuracy, particularly in identifying the no sentiment category, there is still room for improvement in classifying both positive and negative instances. Figure 6: Confusion Matrix of Model Performance. Figure 7 shows the classification report for the lecturers, which includes various evaluation metrics. This report covers precision, recall, F1-score, and support for each class. In class 0, the precision, recall, and F1-score are recorded at 1.00, with the backing of 484 documents. Class 1 shows similar results, where precision, recall, and F1-score also reach 1.00 with support from 182 documents. Meanwhile, for class 2, there is a slight decrease in precision, which reaches 0.97, but recall and F1-score remain high at 1.00 and 0.99, with support from 34 documents. In addition, the overall model accuracy reaches 1.00, indicating that the model is highly effective in classifying the data.