Budi Prasetiyo, Alamsyah, M. Faris Al Hakim, Jumanto, Mahargjo Hapsoro Adi
Computer vision is an area of machine learning that enables computers and systems to take actions and make recommendations based on information from digital images, videos, and other visual inputs. The purpose of this paper is to discuss the selection of optimal parameters in car classification training. This paper discusses computer vision training using the CNN algorithm on vehicle miniature classification. One of the factors that affect the performance of Convolutional Neural Network (CNN) is the selection of appropriate parameters. The data we use comes from the primary data we collect. We took photos of 3 types of vehicles, namely buses, cars, and motorcycles with a total of 486 instances. Next we do data preprocessing by resizing 256*256px. Then we conducted various trials of data augmentation i.e: including "horizontal", "vertical", "horizontal and vertical". We experimented with different augmentations to get the highest accuracy. We present several variations of the epoch to identify the best model of CNN. The accuracy results obtained are 97.34%. © 2023 Author(s).
Department of Computer Science, Universitas Negeri Semarang, Semarang, Indonesia; Administration of Academic and Student Sub Section, Universitas Negeri Semarang, Semarang, Indonesia
