  The biggest challenge faced by a Machine Learning Engineer is the lack of
data they have, especially for 2-dimensional images. The image is processed to
be trained into a Machine Learning model so that it can recognize patterns in
the data and provide predictions. This research is intended to create a
solution using the Cycle Generative Adversarial Networks (GANs) algorithm in
overcoming the problem of lack of data. Then use Style Transfer to be able to
generate a new image based on the given style. Based on the results of testing
the resulting model has been carried out several improvements, previously the
loss value of the photo generator: 3.1267, monet style generator: 3.2026, photo
discriminator: 0.6325, and monet style discriminator: 0.6931 to photo
generator: 2.3792, monet style generator: 2.7291, photo discriminator: 0.5956,
and monet style discriminator: 0.4940. It is hoped that the research will make
the application of this solution useful in the fields of Education, Arts,
Information Technology, Medicine, Astronomy, Automotive and other important
fields.
