The development of the Generative Adversarial Network is currently
very fast. First introduced by Ian Goodfellow in 2014, its development has
accelerated since 2018. Currently, the need for datasets is sometimes still lacking,
while public datasets are sometimes still lacking in number. This study tries to add
an image dataset for supervised learning purposes. However, the dataset that will
be studied is a unique dataset, not a dataset from the camera. But the image dataset
by doing the augmented process by generating from the existing image. By adding
a few changes to the augmentation process. So that the image datasets become
diverse, not only datasets from camera photos but datasets that are carried out with
an augmented process. Camera photos added with painting images will become
still images with a newer style. There are many studies on Style transfer to produce
images in drawing art, but it is possible to generate images for the needs of image
datasets. The resulting force transfer image data set was used as the test data set for
the Convolutional Neural Network classification. Classification can also be used to
detect specific objects or images. The image dataset resulting from the style
transfer is used for the classification of goods transporting vehicles or trucks.
Detection trucks are very useful in the transportation system, where currently many
trucks are modified to avoid road fees.