## 3 Results and Discussion  
The three-dimensional CNN model is trained with two main hyperparameters, namely: epoch, which is the complete iteration of convolution feed-forward before starting over the next iteration; and validation-split, which is the proportion of the training data used for validating the result of the training. In this research, we use the combination of training/validation split of 70/30, 80/20, and 90/10 with epochs of 100 and 150 iterations. The elapsed time and resulting accuracy for each combination are shown in Table 2 and the graphic plot in Figure 10. Accuracy is defined as the percentage of correct predictions for the test data calculated by dividing the number of correct predictions by the number of total predictions, while elapsed time counts the total time needed to perform the training with the corresponding proportion and epochs. Figure 9: Workflow of the research. During model testing with 100 epochs, the algorithm quickly reaches 100% training accuracy under 40 epochs, and in the first half of the epoch, the testing accuracy increases, but in the second half, it does not rise significantly, being around 0.667; 0.672; and 0.685 for 70/30; 80/20; and 90/10 data split. The overall accuracy achieved by the model is between 0.667 to 0.692 for 100 epochs completed between 140 and 183 min. Root Mean Square Error (RMSE) for 70/30 and 80/20 is around 0.28, while for 90/10 is lower at 0.2, which is consistent with higher accuracy. For 150 epochs, the accuracy of 0.672; 0.692; and 0.674 with RMSE of 0.288; 0.314; and 0.296 for the corresponding data split in 70/30; 80/20; and 90/10 ratios, respectively. The process was completed in 4 h and 3 min. Figure 10 shows that the validation accuracy quickly becomes stable after 20 to 25 epochs while the training accuracy is still increasing until it reaches 100%. This condition indicates that the model was overfitting during testing. Overfitting resulted from a vast set of neural connections, which often reduces the system fitness due to non-common cases included in learning data [55]. We readjusted the model to eliminate and reduce overfitting and then tested it with similar hyperparameters. Overfitting correction is performed by randomly deactivating some neurons on each layer, so they are not used during forward- and back-propagation training. This causes the learning process to spread out connection weights without \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No. of Epochs**} & \multirow{2}{*}{**Performance Metrics**} & \multicolumn{3}{c}{**Training/Validation Split**} \\  & & **70/30** & **80/20** & **90/10** \\ \hline \multirow{3}{*}{100} & Time & 163 min & 188 min & 140 min \\  & Accuracy & 0.667 & 0.659 & 0.685 \\  & RMSE & 0.284 & 0.282 & 0.203 \\ \hline \multirow{3}{*}{150} & Time & 243 min & 257 min & 302 min \\  & Accuracy & 0.672 & 0.692 & 0.674 \\ \cline{1-1}  & RMSE & 0.288 & 0.314 & 0.296 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The elapsed time, accuracy, and RMSE of the 3D-CNN model. Figure 10: Graphic plot from the testing and validation of the 3D-CNN model: (**a**) 100 epochs, 70/30 split; (**b**) 100 epochs, 80/20 split; (**c**) 100 epochs, 90/10 split; (**d**) 150 epochs, 70/30 split; (**e**) 150 epochs, 80/20 split; and (**f**) 150 epochs 90/10 split. focusing on specific neurons. In this research, the deactivation probability is set at 0.5, which means there is an equal chance of each unused neuron in the learning process. Low deactivation probability will not reduce overfitting, while high probability will cause the system to underachieve. Reducing neurons results in a smaller, simpler, and more regulated connection network, which means outlying or widely different results will be disregarded. In this manner, the overall error could be reduced by averaging errors from different connections.