## 3 Results
### Aspect Extraction  
The training process in ABSA is iterative and requires careful experimentation to find the exemplary model architecture and optimal parameter set to achieve the desired results [17]. The model's success in accurately classifying aspects and sentiments depends heavily on the quality of the training data, the model design, and the training process. Therefore, each stage of training must be carefully monitored and adjusted so that the model can understand relevant patterns in the data and produce more precise results. During the training process, the model is repeatedly tested on combinations of hyper-parameters, such as learning rate, batch size, and number of epochs, to achieve optimal performance [25]. Each training iteration provides insight into how specific parameters affect the model's ability to recognize aspects and sentiments. For example, a learning rate that is too high can make the model unstable, while a learning rate that is too low can take too long and be ineffective. Once the model is well-trained, the next step is to test it using data that have never been seen before. This testing is critical because it provides insight into the model's ability to predict aspects and sentiments when applied to accurate data. The test results indicate the model's fidelity in real-world situations, such as diverse and dynamic student reviews. The results of this testing process indicate the model's accuracy and help identify areas where the model may struggle or produce misclassifications [26]. Suppose the model performs well on the test data. In that case, it indicates that it can generalize well and is ready to be applied to real-world applications, such as automatically analyzing student reviews on educational platforms. However, if the performance is low, further adjustments are needed either to the training data, model architecture, or hyper-parameters used. This figure illustrates the model training flow from start to finish, including critical steps such as data preprocessing, determining hyper-parameters, and the training and testing process. This organized structure allows for a more efficient training process and helps document the critical steps that play a role in the model's success [27]. The training process of the aspect extraction model carried out in this study is shown in Figure 4.  
Figure 4 shows that the results of the model training process have increased significantly. The aspect extraction model training process with epoch = 5 produces the highest accuracy value. The plot presented shows the training history of the machine learning model, focusing on the accuracy of the training and validation datasets for five epochs. This type of plot is essential for evaluating the performance and ability of the model to generalize data during the training process. X-axis (Epoch): This axis shows the number of epochs or iterations of model training. One epoch is one complete training process on the entire dataset. In this plot, there are five epochs, ranging from 0 to 4. Y-axis (Accuracy): The Y-axis shows the model accuracy and the proportion of correct predictions from all predictions made. Accuracy values range from 0.0 to 1.0, with 1.0 representing 100% accuracy or perfect predictions. Training Accuracy (Blue Line) The blue line depicts the model accuracy on the training dataset over several epochs. Training accuracy starts relatively high and continues to increase gradually with each epoch. By the end of the fifth epoch, the training accuracy is approaching 1.0, indicating that the model predicts the training data very well and increasingly understands patterns in the training dataset over time. Validation Accuracy (Yellow Line) The yellow line shows the model's accuracy on the validation dataset throughout the epochs. Validation accuracy also starts relatively high but is consistently lower than the accuracy on the training dataset. There is a gradual increase in validation accuracy, although it tends to plateau after the first few epochs.