## 3 Results
### Sentiment Classification  
At this stage, the sentiment classification model is being developed, with efforts to fine-tune using hyper-parameters to produce the best sentiment classification model [33]. During training, optimization focuses on key hyper-parameters such as learning rate, batch size, and number of epochs to achieve accurate and efficient classification results [17]. This approach allows the BERT model to identify sentiment nuances in Indonesian student feedback more effectively, strengthening in-depth analysis of relevant sentiment aspects. The training process of the sentiment classification model is shown in Figure 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Epoch** & **Batch Size** & **Dropout** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline
3 & 16 & 0.1 & 0.845 & 0.891 & 0.825 & 0.821 \\
3 & 16 & 0.2 & 0.848 & 0.882 & 0.871 & 0.852 \\
3 & 16 & 0.3 & 0.837 & 0.867 & 0.848 & 0.836 \\
3 & 16 & 0.5 & 0.912 & 0.932 & 0.964 & 0.913 \\
3 & 32 & 0.7 & 0.924 & 0.949 & 0.912 & 0.915 \\
3 & 32 & 0.9 & 0.965 & 0.930 & 0.925 & 0.922 \\
5 & 32 & 0.1 & 0.896 & 0.872 & 0.844 & 0.896 \\
5 & 64 & 0.3 & 0.953 & 0.949 & 0.925 & 0.914 \\
5 & 64 & 0.5 & 0.921 & 0.901 & 0.954 & 0.913 \\
5 & 64 & 0.1 & 0.973 & 0.976 & 0.968 & 0.952 \\
5 & 64 & 0.3 & 0.935 & 0.971 & 0.971 & 0.915 \\
5 & 64 & 0.5 & 0.965 & 0.960 & 0.918 & 0.922 \\
3 & 16 & 0.1 & 0.845 & 0.891 & 0.825 & 0.821 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The results of aspect extraction. Figure 5: The training process of sentiment classification. Figure 5 shows the training history graph of a sentiment classification model, with two lines representing the training accuracy and validation accuracy over the number of epochs. Here is a detailed explanation of the elements in this graph: Y-axis (Accuracy): The vertical axis represents the accuracy value, ranging from 0 to 1. The higher the accuracy value, the better the model correctly classifies sentiments on the training and validation data. X-axis (Epochs): The horizontal axis represents the number of epochs and complete cycles once the model is trained with the entire training data. Each epoch helps the model refine its weights to improve accuracy. Blue Line (Training Accuracy): The blue line represents the training accuracy, which is the model's performance on the training data during the learning process. It can be seen that the training accuracy increases sharply in the early epochs and reaches an accuracy level close to 1 or 100% at around epoch five and beyond. This indicates that the model has successfully learned the patterns in the training data. Yellow Line (Validation Accuracy): The yellow line shows the accuracy of the validation data, which is the data that the model was not trained on directly but is used to evaluate the model's generalization performance. Validation accuracy increases steadily in the early epochs but is not as high as training accuracy. This is a common sign in machine learning model training, where validation accuracy is usually lower than training accuracy because the model is potentially overfitting the training data. Overfitting Trend: After the 4 th epoch, the training accuracy increases until it almost reaches 1, while the validation accuracy stops rising significantly. Indicates potential over-fitting, where the model tends to be too specific to the training data, and the performance on the validation data does not experience a comparable increase. The resulting model has been evaluated, and the detailed results are shown in Table 13.  
As shown in Table 13, the selection of hyperparameters significantly impacts the model's performance in sentiment classification. The dropout rate of 0.4 achieves an optimal balance between reducing overfitting and maintaining the model's generalization ability, as indicated by the F1-score of 0.974. This configuration demonstrates the model's capability to handle noisy data without losing critical information.