In taking pictures using a camera, for example, it often results in poor image quality. The resulting image quality can be in the form of black and white images. This can interfere with further processing of the image. Specifically, this can happen in the agricultural sector, such as the results of a bad potato leaf image (black and white). In fact, the images of leaves from agricultural land will be used for image processing which can help with the planting process, for example. This is of course very disturbing, so a way is needed to translate a gray scale image into an image that has a color resembling the shape of an actual leaf. In fact, the development of Deep Learning with various models has grown rapidly, one of which is the Conditional GAN which can process image to image translation. Seeing the purpose of the Conditional GAN, this paper implements the Pix2Pix model based on the Conditional GAN which aims to translate black and white images into images that have color. This experiment produces images with colors that match the original image with good quality. So that with the results of this experiment, it is hoped that problems with taking images that are sometimes not good enough can be solved by image translation using deep learning.
 

Bank XYZ is one of the banks in Kupang City, East Nusa Tenggara Province which has several ATM machines and is placed in several merchant locations. The existing ATM machine is one of the goals of customers and non-customers in conducting transactions at the ATM machine. The placement of the ATM machines sometimes makes the machine not used optimally by the customer to transact, causing the disposal of machine resources and a condition called Not Operational Transaction (NOP). With the data consisting of several independent variables with numeric types, it is necessary to know how the classification of the dependent variable is NOP. Machine learning approach with Logistic Regression method is the solution in doing this classification. Some research steps are carried out by collecting data, analyzing using machine learning using python programming and writing reports. The results obtained with this machine learning approach is the resulting prediction value of 0.507 for its classification. This means that in the future XYZ Bank can classify NOP conditions based on the behavior of customers or non-customers in making transactions using Bank XYZ ATM machines.