<![CDATA[The development of technology in the field of computer vision in recent years with the application of Technological developments in the field of computer vision in recent years with the application of convolutional neural networks have shown sophisticated performance with a high level of accuracy, such as object detection. The problem in the world of computer vision that has been looking for a solution for a long time is object classification in general images. How to duplicate the human ability to understand images, so that computers can recognize objects in images like humans. Therefore, the need for deep learning is one branch of machine learning where the algorithm used is inspired by the workings of the human brain. Some people may be more familiar with Convolution Neural Network. CNN is used to recognize and classify patterns in handwriting. The network assumes that the input used is an image. The network has a special layer called the convolution layer. In this layer, the images are inserted according to the predefined filters. In this study, various combinations of CNN architectural designs were carried out such as the number of convolution layers, stride size, number of epochs, type of kernel size optimizer. The research data comes from the National Institute of Standards and Technology (NIST) database, then the data is divided into three, namely 60% training data, 20% validation and 20% testing. The results of this experiment produce a very good accuracy value using 2 convolution layers, 50 epochs, with Adam optimizer producing an accuracy value of 99.5% when testing the model. Then evaluate the model using the confusion matrix, assigning a high value with an average value of 100% accuracy, while for the average value of precision with a value of 100%, for an average recall value of 100%, and finally an average value of f1 score of 100%.]]>
