Artificial Intelligence-Based Driver Drowsiness Alarm System for Real-Time Monitoring - Gunawan; Achmad Yani; Heri Trisna Frianto; Junaidi (Journal of Information Technology, computer science and Electrical Engineering, 2026)
This research aims to design and build a drowsy driver detection alarm that uses Android-based artificial intelligence. Drowsy drivers are one of the factors that cause serious and potentially fatal traffic accidents. Therefore, it is necessary to develop a system that can detect the signs of drowsy drivers and provide timely warnings to prevent accidents. In this study, we implemented artificial intelligence technology to detect signs of drowsy drivers based on data analysis such as eye movements, head position, and driver activity. The system uses sensors and cameras on Android devices to monitor and analyze driver behavior in real-time. The designed system will alert the driver if signs of drowsiness are detected. The alert can be in the form of sound, vibration, or visual display on the Android device's screen. In addition, the system can also record and report drowsy driver detection data to related parties, such as vehicle owners or traffic control centers. The software development method used in this study is the software development lifecycle model (SDLC) with the stages of needs analysis, design, implementation, testing, and maintenance. We also used machine learning techniques to train sleepy driver detection models based on the data collected. The result of this study is a drowsy driver detection alarm system that can be integrated with Android devices. These systems can help prevent traffic accidents caused by drowsy drivers by providing timely and effective alerts.This research aims to design and build a drowsy driver detection alarm that uses Android-based artificial intelligence. Drowsy drivers are one of the factors that cause serious and potentially fatal traffic accidents. Therefore, it is necessary to develop a system that can detect the signs of drowsy drivers and provide timely warnings to prevent accidents. In this study, we implemented artificial intelligence technology to detect signs of drowsy drivers based on data analysis such as eye movements, head position, and driver activity. The system uses sensors and cameras on Android devices to monitor and analyze driver behavior in real-time. The designed system will alert the driver if signs of drowsiness are detected. The alert can be in the form of sound, vibration, or visual display on the Android device's screen. In addition, the system can also record and report drowsy driver detection data to related parties, such as vehicle owners or traffic control centers. The software development method used in this study is the software development lifecycle model (SDLC) with the stages of needs analysis, design, implementation, testing, and maintenance. We also used machine learning techniques to train sleepy driver detection models based on the data collected. The result of this study is a drowsy driver detection alarm system that can be integrated with Android devices. These systems can help prevent traffic accidents caused by drowsy drivers by providing timely and effective alerts.