## 3 Results  
### Testing Results  
The experiment was carried out by varying the \(\lambda_{1}\) and \(\lambda_{2}\), which are the weight for L\({}_{1}\) and TV terms. Other parameters, such as the number of epochs and the dataset, will be equalized. The number of paired data used was 1365, divided into training and validation datasets. The selection of the validation dataset was done randomly and the number of datasets to be used for validation was 10. This number is relatively very small because the GAN generative model requires a lot of data for the training process. In some references, the paired data used in the training process number from tens to tens of thousands [7; 10; 11; 12; 28]. Therefore, in the experiment, we maximized the number of paired data for the training process; the cGAN-TV results can be seen clearly and analyzed correctly. In addition, we also prepared 68 testing datasets containing images of real Indonesian people obtained from photographs of students from the Department of Electrical Engineering, University of Indonesia, and several images from compiled datasets. The 68-testing dataset was used because it refers to the GAN study conducted by [PERSON] et al. [21] where they used 5% testing of the total training data. The training stage took approximately two hours and was carried out for 40,000 epochs, where, for every 1000 epochs, we displayed the temporary results of the cGAN-TV using one of the validation sketch images. This aimed to monitor the progress of the cGAN-TV more efficiently so that the training process could be manually stopped if it produced unexpected results, such as over smoothing. The training process for each scenario was carried out one by one, and after each model was saved, the SSIM and FID values were calculated. The test results for each scenario can be seen in Table 3. The eleven scenarios were carried out in three phases. The first phase (a-f) aimed to find the optimal value of \(\lambda_{1}\), in which the \(\lambda_{2}\) was set to a small value to avoid the over-smoothing effect. After we achieved the optimal \(\lambda_{1}\), then we stepped into the second phase (g-j) to find the optimal \(\lambda_{2}\) with the constant \(\lambda_{1}\) optimal value. The last stage was to test any higher \(\lambda_{1}\) and \(\lambda_{2}\) values. The visual results of each scenario can be seen in Figure 3.  
It should be noted that the SSIM value was calculated by comparing the grayscale structure of the image generated by the generator and the original image of the sketch. In comparison, the FID value was achieved by comparing the two distributions of the entire validation image generator with the distribution of all the original images. The color difference in the image would greatly affect the FID value while having no impact on the SSIM value. Therefore, every time an evaluation was carried out, the SSIM would not change, while the FID would always change because the generator results would generate random background and skin tones. Based on the test results, it can be seen that the effect of \(\lambda_{1}\) was very significant on the SSIM and FID values. The lowest SSIM value was 0.695, found in the scenario, with a very small \(\lambda_{1}\) value of 0.01, and the largest FID value was achieved in scenario c, where the \(\lambda_{1}\) value was 1. However, when the \(\lambda_{1}\) value was too large, it appears that the SSIM value was actually decreased, and the FID value increased. This is due to overfitting, and the model could not generalize properly. In scenario e, the SSIM value was 0.834, and the FID value was 94.705. When the \(\lambda_{1}\) value increased to 150 with the same \(\lambda_{2}\) value in scenario f, the SSIM dropped to 0.809, while the FID increased to 97.285. The \(\lambda_{2}\) value also had a significant impact, as seen in scenarios e and g, where the SSIM value increased to 0.834 from 0.815, and the FID value decreased to 94.705 from 97.078.