PROTOTYPE SMART TIME SCHEDULER LAMPU LALU LINTAS MENGGUNAKAN ALGORITMA HAAR CASCADE - Neci Nirwanda; Helda Yenni; M. Khairul Anam; Lathifah Lathifah (Jurnal Teknoinfo, 2023)
The traffic light functions as a traffic controller at crossroads and also as smoothness at intersections to avoid congestion. The current application of conventional traffic lights still has weaknesses in determining the duration of traffic lights which are not adjusted to the number of vehicle volumes which often change every time. Especially when there is a dense flow of vehicles at intersections, especially at certain times. Based on observations of traffic density at the intersection that connects Soekarno - Hatta road and Arifin Achmad road in Pekanbaru City. There is often a high density of vehicles from the direction of Jalan Soekarno-Hatta (Morning Market) to Jalan Jenderal Sudirman during working hours so that the duration of the traffic lights cannot reduce the amount of queue density in the light traffic. The implementation of the Smart Time Scheduler is a solution for adjusting the duration of traffic lights based on the level of traffic density, by building a tool to detect and calculate the number of vehicle queues at the Traffic Light and then enter the calculation results into 3 categories, namely Normal, Medium and Dense Density. The system uses Haar Cascade with the open cv library on the Raspberry pi, the results of the system testing that has been carried out can count the number of vehicles and instruct Arduino to set the duration of the traffic lights where in the category of normal vehicle queues, the green traffic light gets a waiting time of 10 seconds in countdown, Moderate category for 15 seconds, and Solid Category, for 20 seconds, while the calculation error value uses the Error Percentage equation formula, namely comparing the results of vehicle calculations by the system with Manual Calculation Results multiplied by 100% Then the System Calculation Error is 16% and the accuracy level of the tool is 100% - 16% = 84%, the size of the error value is influenced by the quality of the light intensity, and the distance of the camera's detection of the object when performing the detection.