Classification of Distracted Driver Using Support Vector Machine Based on Principal Component Analysis Feature Reduction and Convolutional Neural Network

Abstract

The use of ground transportation in Indonesia, especially in major cities like Surabaya, has experienced rapid growth. However, this increased usage has also led to a rise in traffic accidents. One of the main contributing factors is driver distraction. Therefore, this study aims to develop a method for detecting distracted drivers using image classification technology with Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) models. In this research, data was obtained from the "State Farm Distracted Driver Detection" dataset, which contains images of drivers who are either distracted or not focused while driving. The initial process involves data preprocessing, such as resizing images to 50 x 50 pixels and dividing the dataset into training and testing data. Next, feature extraction is performed using a CNN model with three convolutional layers, three Maxpooling layers, and one flattened layer. After feature extraction, the Principal Component Analysis (PCA) method is used to reduce the dimensionality of the data. Furthermore, an SVM model was trained using data reduced by PCA with a 60:40 data split. This research conducted a comparison between the use of PCA and not using PCA. Based on the test results, the use of PCA not only improved the classification accuracy to 96.28% compared to 92.46% without PCA but also accelerated the training time to 10.64 seconds from 19.67 seconds without PCA.