## 3 Methodology
### Hyper-Parameter Optimization
#### 4.2.2 SVM Hyper-Parameter Optimization  
The hyper-parameters of SVM model were configured as shown in Table 19, and model development and accuracy analysis were performed. \begin{table}
\begin{tabular}{c c} \hline
**Category** & **Parameters** \\ \hline ANN & 1, 2, 3, 4 \\ SVM & 1–17 \\ \hline \end{tabular}
\end{table} 
Table 18: Hyper-parameter for optimizing ANN model. Figure 7: Prediction model performance analysis for optimizing number of iterations. In this study, four models were developed according to the type of kernel, and each model was evaluated by applying the cost and gamma configuration using a random research method that sets the maximum and minimum values for cost and gamma and repeats and optimizes random values within the range. The parameters changed in the development of the SVM model are shown in the table. The cost was changed in the range of 0, 1, 2, 4, 8, and 16, and gamma was changed in the range of 0.00001, 0.0001, 0.001, 0.1, and 0. The performance was analyzed in a total of 120 configurations. According to the kernel, it was divided into SVM Model 1 (Radial), SVM Model 2 (Linear), SVM Model 3 (Polynomial), and SVM Model 1 (Sigmoid). Tables 20-23 show the performance of each model, and the shaded cell in each table is the value representing the highest performance in the model. Table 20 shows the performance results for each configuration of Model 1. In Model 1, when the cost was 16 and the gamma was 0, the performance was highest at 0.8. When analyzing the performance of Model 2, in which the kernel was applied linearly, it shows similar performance in most configurations. When the cost was 1,2, and 4, it showed the same performance in all gammas, and there was no change in performance when the cost was 8 and 16. When analyzing the prediction performance through simulation, when the cost was 16, and the gamma was 0.00001, 0.0231 showed the best performance (Table 21). Table 22 presents the performance results for each configuration of Model 3 to which the kernel was applied as a polynomial. In Model 3, when gamma was 0, high performance was shown regardless of cost, and when gamma was 0, a lower cost indicated higher performance. Performance analysis of each combination showed that the highest performance was 11.52 when the cost was 1 and the gamma was 0. As shown in the analysis results in Table 23, Model 4 shows the best performance at 20.84 when the cost is 2, and the gamma is 0.001. In the case of Model 4, the performance was much lower when gamma was 0.1 or 1, so it was not shown in the analysis results. Figure 8: Prediction model performance analysis for hidden layer structure. \begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Parameters** \\ \hline Kernel & Radial, Linear, Polynomial, Sigmoid \\ Cost & 1, 2, 4, 8, 16 \\ Gamma & 0.00001, 0.0001, 0.001, 0.01, 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Hyper-parameter for optimizing SVM model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & \multicolumn{5}{c}{**Cost**} \\ \hline
**Gamma** & **1** & **2** & **4** & **8** & **16** \\ \hline
0.00001 & 51.21 & 47.00 & 50.05 & 51.05 & 53.05 \\
0.0001 & 40.70 & 39.20 & 38.30 & 49.60 & 47.00 \\
0.001 & 50.47 & 49.69 & 48.22 & 46.50 & 44.63 \\
0.01 & 52.99 & 52.94 & 52.85 & 53.03 & 53.02 \\
0.1 & 15.31 & 20.84 & 18.38 & 16.38 & 14.95 \\
0 & 11.52 & 13.52 & 13.41 & 20.28 & 15.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 23: Prediction model performance analysis according to hyper-parameter (SVM model 1). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & \multicolumn{5}{c}{**Cost**} \\ \hline
**Gamma** & **1** & **2** & **4** & **8** & **16** \\ \hline
0.00001 & 51.21 & 49.55 & 47.00 & 43.92 & 41.41 \\
0.0001 & 43.07 & 40.78 & 39.25 & 38.46 & 37.89 \\
0.001 & 24.70 & 20.84 & 32.85 & 33.78 & 26.47 \\
0.01 & 38.27 & 37.65 & 36.12 & 46.72 & 53.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Prediction model performance analysis according to hyper-parameter (SVM model 1).