kironowarni, Nabilla Fawzia (2025) PERANCANGAN SISTEM MONITORING DENGAN KAMERA UNTUK MENDETEKSI KELELAHAN SEBAGAI UPAYA MENINGKATKAN KESELAMATAN BERKENDARA BERBASIS IOT DESIGN OF A MONITORING SYSTEM WITH A CAMERA TO DETECT FATIGUE AS AN EFFORT TO IMPROVE IOT-BASED DRIVING SAFETY. Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.
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Abstract
Driving safety is a crucial issue that is often overlooked, particularly regarding driver fatigue, which can trigger microsleep and lead to fatal accidents. According to data from the Indonesian National Police Traffic Corps (Korlantas Polri)'s IRSMS (Information Systems Monitoring System) in 2024, 79,220 traffic accidents were recorded, with fatigue being a leading cause. Therefore, a real-time drowsiness detection system is needed to minimize the risk of accidents. This study designed and implemented a camera-based and Internet of Things (IoT) monitoring system capable of detecting driver drowsiness using AI-based visual classification of facial expressions (closed eyes and yawning). The system uses an OV5647 camera module connected to Grove Vision AI V2 for live image classification, and an ESP32 microcontroller to control follow-up actions such as activating a buzzer or Telegram notifications. The research methods include image data collection, AI model training (using SenseCraft or Edge Impulse), hardware integration, and simulation testing in a real-world environment. The system design was developed with three levels of drowsiness classification (not drowsy, starting to drowsy, and drowsy), with automatic responses based on the duration and frequency of detection. The successful implementation demonstrated the system's ability to detect drowsiness responsively and accurately, providing effective warnings to drivers. This research is expected to provide a practical technological solution to improve traffic safety in Indonesia. Keywords: Monitoring System, Driver Fatigue, Drowsiness Detection, OV5647 Camera, Grove Vision AI V2, ESP32, IoT, Driving Safety.
| Item Type: | Thesis (Undergraduate) |
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| Uncontrolled Keywords: | Monitoring System, Driver Fatigue, Drowsiness Detection, OV5647 Camera, Grove Vision AI V2, ESP32, IoT, Driving Safety. |
| Subjects: | T Technology > T Technology (General) T Technology > TL Motor vehicles. Aeronautics. Astronautics Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources |
| Divisions: | Fakultas Teknik > Program Studi Teknik Informatika Fakultas Teknik > Program Studi Teknik Informatika > Tugas Kuliah |
| Depositing User: | 1462100025 Nabilla Fawzia Kironowarni |
| Date Deposited: | 12 Jun 2026 07:19 |
| Last Modified: | 12 Jun 2026 07:19 |
| URI: | http://repository.untag-sby.ac.id/id/eprint/42011 |
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