<![CDATA[When developments in the field of computer science are growing rapidly. For example, the development of image or video predictions for various fields has been widely applied to assist further processes. The field of computer vision has created many ideas about processing using deep learning algorithms. Sometimes the problem with using deep learning or machine learning is in the availability of the dataset or the unavailability of the dataset. Various methods are used to add to or enrich the dataset. One way is to add an image dataset by creating a synthetic image. One of the well-known algorithms is Generative Adversarial Networks as an algorithm for generating synthetic images. Currently, there are many variations of the GAN to around 500 variants. This research is to utilize the Cycle GAN architecture in order to enrich the dataset. By doing GAN as a synthetic image generator. This is very important in procuring image datasets, for training and testing models of Deep Learning algorithms such as Convolutional Neural Networks. In addition, the use of synthetic images produces a deep learning model to avoid overfitting. One of the causes of the overfitting problem is the lack of datasets. There are many ways to add image datasets, by cropping, continuously rotating 90 degrees, 180 degrees. The reason for using Cycle Generative Adversarial Networks is because this method is not as complicated as other GANs, but also not as simple. Cycle GAN synthetic images are processed with Super Resolution GAN, which aims to clarify image quality. So that it produces a different image and good image quality. ]]>
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