## 4 Results
### GPCR Classification Performance with Virtual Class  
Table 6 shows the performance for the classification of GPCR using SVM with virtual class implementation at the subfamily level. First and foremost, the results are considerably \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Feature Extraction Method** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F-Score** \\ \hline PseAAC & 85.3 & 87.7 & 87.7 & 0.877 \\ FAPSO & 82.8 & 88.9 & 82.8 & 0.858 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: GPCR classification performance for subfamily level. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Feature Extraction Method** & **Accuracy (\%) Method** & **Precision (\%)** & **Recall (\%)** & **F-Score** \\ \hline PseAAC & 97.7 & 97.7 & 97.7 & 0.977 \\ FAPSO & 97.9 & 97.9 & 97.9 & 0.979 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: GPCR classification performance for family level. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Feature Extraction Method** & **Accuracy (\%) Method** & **Precision (\%)** & **Recall (\%)** & **F-Score** \\ \hline PseAAC & 76.9 & 86.2 & 77.4 & 0.816 \\ FAPSO & 71.6 & 87.5 & 81.2 & 0.842 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: GPCR classification performance for sub-subfamily level. different between PseAAC+VC and FAPSO+VC. FAPSO+VC gives better performance than PseAAC+VC. When comparing PseAAC against PseAAC+VC, the latter has higher accuracy, precision, recall and F-score values than the former, at 85.4%, 88.3%, 88.3%, and 0.833. As for FAPSO and FAPSO+VC, the FAPSO+VC also performs better than FAPSO. FAPSO+VC achieved 86.9% accuracy, 94.1% precision, 90.1% recall, and 0.921 F-score. At the subfamily level, it was found that false positive numbers had decreased for the FAPSO algorithm compared to the PseAAC algorithm after VC implementation. This is because these data have been classified at the VCs, which are in classes A, B, C, D, or E and therefore avoid the error propagation problem. There is also an increase in the true positive number of classes A and C, in which the data were classified accurately after VC implementation. As for the performance at the sub-subfamily level shown in Table 7, when comparing PseAAC against PseAAC+VC, the latter has better accuracy, precision, recall and F-score values than the former, at 78.8%, 89.6%, 82.6%, and 0.86, respectively. As for FAPSO and FAPSO+VC, the FAPSO+VC also performs better than FAPSO. FAPSO+VC achieved 81.3% accuracy, 92.1% precision, 85.1% recall, and 0.844 F-score. At sub-subfamily level, false positive numbers have also decreased especially for sub-subfamily under subfamily A amine, A hormone, A Peptide and C CalcSense. It is found that the number of positive has increased especially for the subfamily A amine, A Hormone and C CalcSense. It can be seen that virtual class consistently increased performance in all experiments, since it increases the likelihood of avoiding error propagation in the hierarchy by stopping the classification at an appropriate level. Table 8 compares the GPCR protein classification accuracy performance obtained from this study and earlier research that made use of the same GDS data set. This study's accuracy value at the family level was determined to be 97.9%, which is remarkably similar to the accuracy values in the studies of [57; 61; 63; 64; 66]. In this study, the second level of the hierarchy had an accuracy value of 86.9%. Nevertheless, [63] obtained higher accuracy values for the second level of the hierarchy, which is 89.2%. The accuracy percentage at the third level obtained by this study is 81.3%. Still, the research of [63], which received an accuracy value of 90.9%, produced the highest accuracy value for this third level.