%0 Thesis
%9 Skripsi
%A Mochamad Ghifari ‘Azmi, NIM.: 16650051
%B Fakultas Sains dan Teknologi
%D 2020
%F digilib:42684
%I UIN SUNAN KALIJAGA YOGYAKARTA
%K Vehicle Counting, Sistem Otomatisasi, Convolutional Neural Network
%P 112
%T SISTEM OTOMATISASI PENGHITUNGAN KENDARAAN (VEHICLE COUNTING) BERBASIS CONVOLUTIONAL NEURAL NETWORK
%U https://digilib.uin-suka.ac.id/id/eprint/42684/
%X The growth of motor vehicle users is increasing every year. As vehicle users   grow, road traffic volume also increases. To manage traffic properly, monitoring of  traffic flow is certainly needed. Traffic flow monitoring can be seen from the  number of motorized vehicles passing a road. But the process of counting the  number of motorized vehicles manually is limited to the accuracy of human vision.  Currently, almost every intersection in Indonesia, especially CCTV / ATCS  cameras have been installed, therefore the author sees the possibility of utilizing the  video from the camera to be processed in order to generate traffic flow monitoring  data automatically.  This research was started by collecting data from CCTV / ATCS of the  Department of Transportation of Sukoharjo Regency and after that the image preprocessing    was done with cropping. Followed by object detection, tracking and  counting or vehicle counting. The vehicle calculation in this video is done using the  convolutional neural network architecture.  The results of this study conclude that if you want to count cars, be it trucks,  buses, minibuses, sedans or pickups, the COCO dataset is suitable to use the truck,  car and bus classes. Meanwhile, to calculate the motorbike is not suitable, and  finally, if you want to count all the vehicles passing on the road, be it bicycles,  motorbikes, trucks, buses, pickups and sedans, the COCO dataset is also not  suitable for application in the Sukoharjo area and in general Indonesia.
%Z Pembimbing : Nurochman, S.Kom., M.Kom.