## 4 Result and Discussion
### Answer-Grading Process after Balancing Dataset  
These experiments will use a new dataset with the additional data through data augmentation with the GPT-3.5 and GPT-4 models. We will conduct various experiments with existing model combinations, while fine-tuning the hyperparameters. From the several experiments that have been carried out, we will present the best results among them. The details of the scenarios conducted can be seen in Table 5. Exp 1 and Exp 5 represent the best scenarios for experiments using the original Mohler dataset, for which the results can be seen in Table 3. Removing stopwords is not included in the parameter combination because the experimental results are not significant, so it was not involved. Each scenario is executed with a fixed parameters set based on the results of previous experiments. The results of this experiment will be evaluated using the evaluation metrics mentioned earlier, namely RMSE, F1-score, accuracy, and Pearson correlation. These experiments include the SentenceTranformers model mentioned in Table 1.  
Figure 8 displays the RMSE values from our eight recommended models when implemented on three datasets: Original Mohler, BalMohler-3.5, and BalMohler-4, represented as Ori, Aug-3.5, and Aug-4, respectively, for each subsequent figure. Generally, better results are obtained from using BalMohler-3.5 and implementing the all-distilroberta-v1 model. To shorten the model names, what is shown in the figure is the abbreviation for each model based on Table 1. \begin{table}
\begin{tabular}{c c c c} \hline
**Augmentation Model** & **Grade Category** & **Cosine Similarity** & **METEOR** \\ \hline GPT-3.5 & False & 0.4369 & 0.59722 \\ GPT-3.5 & Correct & 0.8040 & 0.59309 \\ GPT-4 & False & 0.3645 & 0.59309 \\ GPT-4 & Correct & 0.8080 & 0.59309 \\ \hline \end{tabular}
\end{table} 
Table 4: Data augmentation result evaluation. \begin{table}
\begin{tabular}{c c c c} \hline
**Experiment** & **Pre-Processing** &
\begin{tabular}{c} **Gradient** \\ **Checkpointing** \\ \end{tabular} & **Split Size** \\ \hline Exp 1 & SpCh LC & Yes & 70–30 \\ Exp 2 & SpCh LC & No & 70–30 \\ Exp 3 & SpCh LC & Yes & 80–20 \\ Exp 4 & SpCh LC RS & Yes & 80–20 \\ Exp 5 & SpCh LC & No & 80–20 \\ \hline \end{tabular}
\end{table} 
Table 5: Detail experiment scenario. The best RMSE score of 0.39913 was obtained from the implementation of Exp 3, using the all-distilroberta-v1 model with a new balanced dataset from data augmentation with the GPT-4 model. However, in general, using BallMohler-3.5 results in a smaller average RMSE score. Meanwhile, the worst RMSE scores were obtained from implementing Exp 5 on the bert-base-uncased model with BalMohler-4, with a value of 5.33808. Figure 9 displays the experimental results based on the F1-score score obtained. Overall, balancing the dataset by implementing data augmentation improves the performance of the grading system. Figure 8: Comparing RMSE for all models and dataset. Figure 9: Comparing F1-score for all models and datasets. The best F1-score result of 0.91886 was obtained from Exp 3 with BalMohler-4 and the implementation of the all-distilroberta-v1 model. Similar to the RMSE results, on average, the F1-score results from using BalMohler-3.5 are better than BalMohler-4. The worst result, 0.2391, comes from Exp 5, using the BalMohler-4 dataset and the bert-base-uncased model. This worst value is close to the worst F1-score value with the Original Mohler dataset (0.2379). Figure 10 displays the performance evaluation in terms of accuracy. Similar to the other evaluation metrics, the resulting balanced dataset from implementing GPT data augmentation generally improves the performance of the grading system. The best accuracy result of 0.91969 was obtained from Exp 3 with the implementation of the BalMohler-4 dataset and the all-distilroberta-v1 model. On average, the accuracy value of BalMohler-3.5 implementation is also better than the implementation of BalMohler-4.