Implementation of the Advanced Traffic Management System using k-Nearest Neighbor Algorithm

With the rapid population growth in Indonesia, the development of traffic becomes congested; various ways have been done to overcome traffic jams, one of which is by using the Advanced Traffic Management System (ATMS), which is a development branch of the Intelligent Transportation System (ITS) and uses the k-Nearest Neighbor (k-NN) algorithm which is an algorithm that uses the object classification method based on learning data that is the closest distance to the object. Overcoming traffic jams is a problem that must be faced, especially at certain hours. Therefore, to overcome this, it is necessary to have traffic lights to drive in an orderly manner. To simulate, it can use one simulation software, namely SUMO (Simulation of Urban Mobility). Using simulation software to simulate traffic density at the intersection, it is hoped that it can visualize the traffic intersection state, breaking down the thickness. Using the Simulation of urban mobility software, it is expected to simulate traffic density to break down congestion at traffic intersections with data obtained from the k-NN algorithm data training. Measurements used using error rate measurements include Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) as well as Time Series Prediction.