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However, in tests with low light intensity (10-25 lux), the accuracy dropped to around 87-90%, with some detection errors (false negatives) and slower response times. This system is implemented in the form of an Android application with an easy-to-use interface, capable of providing alerts through sound, vibration, flashing lights, and notifications. With these results, the application has proven to be reliable in detecting driver drowsiness, although it still has limitations in low light conditions. This application is expected to help reduce the risk of traffic accidents caused by drowsy driving.","\u003Cp>Drowsiness is one of the main causes of traffic accidents that can result in fatalities. Therefore, a system is needed that can directly detect driver drowsiness and provide early warnings. This study aims to design and build a drowsiness detection system using image processing and machine learning with the Convolutional Neural Network (CNN) method. 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