Analisis Deteksi Mata Kantuk Di Wajah Pengemudi Menggunakan Support Vector Machine (SVM) Berbasis Citra Real-Time - Maharani, Ullya Dwi; Handayani, Ade Silvia; Lindawati, Lindawati (Forum Kerjasama Pendidikan Tinggi, 2024)
Traffic accidents in Indonesia are a serious issue with a high number of fatalities, and one of the main causes is microsleep, which is a brief moment of sleep while driving. To address this problem, this research has developed a sleePIness detection system based on the Internet of Things (IoT) using a Raspberry PI and a webcam, utilizing the Support Vector Machine (SVM) algorithm. The system is designed to detect the driver’s eye condition and provide a warning through a buzzer if the eyes are closed for more than 3 seconds. The research results indicate that the SVM model with a polynomial kernel has a training accuracy of 85.04%, demonstrating its ability to classify eye data into