IMPLEMENTASI DEEP LEARNING MENGGUNAKAN ALGORITMACONVOLUTIONAL NEURAL NETWORK GUNA MENGETAHUIKUALITAS BAN KENDARAAN - Alvin Putra Perdana (2024)
Nowadays, having a vehicle is a necessity that must be owned by the society to simplify traveling from the start location to the final destination. Based on data from the Central Bureau of Statistics, the latest trend regarding data on the number of motorcycles in 2021 is 120,042,298 units and data on the number of passenger car in 2021 is 16,413,348 units. As the number of vehicles on the road increases, this is inseparable from the risk of traffic accidents. The phenomenon of motorized vehicles using bald tires is still often found. Some possible accidents caused by the use of bald tires are sliding, blown tires, and vulnerable to impacts from poor road conditions. In dealing with this phenomenon, tire quality can be classified using Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms with ResNet50 Architecture. The data used are the data from direct acquisition in the field, with a total of 400 images divided into 2 classes. The steps start from preprocessing, feature extraction, data splitting and others. The data splitting performed is 80:20 with 320 train data. The performance obtained for the Support Vector Machine (SVM) is 87.5% with a polynomial kernel. However, after implementing Deep Learning, the performance of the model to classify vehicle tires increased to 100%.