Deteksi Kerusakan Ban Mobil Menggunakan Convolutional Neural Network dengan Arsitektur ResNet-34

The examination of tire damage can be categorized as part of vehicle maintenance with the aim of ensuring the tires are in good condition. Visual inspection using human observation has limitations, making it not always accurate and prone to errors in determining tire roadworthiness. This study designs a machine learning model using Convolutional Neural Network (CNN) with a ResNet-34 architecture to detect car tire damage. The parameters used in training this CNN model include the Adam optimizer, a learning rate of 0.0001, batch size 32, and 50 epochs. In this study, there are two predicted image classes: normal tires and damaged tires. The research results indicate that the CNN model with ResNet-34 architecture can predict both classes very well, as evidenced by the model evaluation results with an accuracy of 0.916, precision of 0.907, recall of 0.927, and an F1 score of 0.917. These results suggest that the CNN model with ResNet-34 architecture can be used as an effective tool for inspecting tire damage.