MACHINE LEARNING DETEKSI KANTUK PENGEMUDI MENGGUNAKAN FACIAL NERVE GRADING SYSTEM 2.0 (FNGS2.0) PADA FLATFORM THINKSPEAK: MACHINE LEARNING DETEKSI KANTUK PENGEMUDI MENGGUNAKAN FACIAL NERVE GRADING SYSTEM 2.0 (FNGS2.0) PADA FLATFORM THINKSPEAK - Timbo Faritcan Parlaungan S.; Dimas Juliana Tri N (STMIK SUBANG, 2022)
Traffic accidents are one of the negative impacts of technological advances in the field of transportation. The increase in traffic accidents is caused by several factors where the highest factor causing traffic accidents is human/HR. Driver fatigue is a significant factor in a large number of traffic accidents, therefore, to minimize it, an alarm system is needed to detect sleepiness in real time. In this study, a sleepy eye detection system will be made based on Facial Landmarks Detection which is the development of the Facial Nerve Grading System 2.0 (FNGS2.0) using the Eye Aspect Ratio (EAR) and Mouth viewpoint proportion (MAR) methods with the Thingspeak platform which is implemented into Orange Pi PC. The input of this system is video recorded from the webcam in real time. The output of this system uses the speaker as an alarm to give a warning that the driver is detected as sleepy. The system made in this study shows that it can detect sleepy eyes well. The test results show that the system can detect 93.3% of the eyes, 96.7% of eye blinks, and 95% of the mouth.