Payment system at the toll gate has been improved, from using physical money replace with e-money. The system needs to which class types of the vehicle entering the toll gate so the system can know how much will it take from the e-money. There are five class types of vehicles, but there are still many toll gates that have high limit to limit the class of vehicles that can enter, making it difficult for class types other than the first class type because they only have a few gates. This research uses You Only Look Once and Convolutional Neural Network as its methods. You Only Look Once is used to detect the location of the vehicle in the image. Convolutional Neural Network is used to classify the class types of the vehicle in the image. For convolutional neural network model, one well-known model is VGG16 which is good in classifying images. The result of this research that will be displayed is the classified of the class type of the vehicle in the form of strings. The result from tests that were done is an accuracy of 93.5% and f-score of 81.37% from self-configuration convolutional neural network and an accuracy of 90.76% and f-score of 73.53% for VGG16 model.


    Access

    Download


    Export, share and cite



    Title :

    Pengenalan Golongan Jenis Kendaraan Bermotor pada Ruas Jalan Tol Menggunakan CNN


    Contributors:

    Publication date :

    2020-04-22


    Remarks:

    Jurnal Infra; Vol 8, No 1 (2020); 196-202


    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Estimasi Beban Pencemar Dari Emisi Kendaraan Bermotor di Ruas Jalan Kota Pekanbaru

    Hodijah, Nurhadi / Amin, Bintal / Mubarak, Mubarak | BASE | 2014

    Free access


    Pengenalan Plat Kendaraan Bermotor dengan Menggunakan Metode Template Matching dan Deep Belief Network

    Michael, Michael / Tanoto, Frenky / Wibowo, Eric et al. | BASE | 2019

    Free access

    Model Prediksi Kecelakaan Kendaraan Sepeda Motor pada Ruas Jalan di Kota Ambon

    Frando Simon Hukom / Ludfi Djakfar / Muhammad Zainul Arifin | DOAJ | 2023

    Free access