The license plate of the vehicle is unique and is only owned by one vehicle per vehicle plate series, to make it easier for the police, especially the traffic police, to track traffic violators through the vehicle number plate. The Deep Belief Network algorithm works by processing the dataset through 3 stages, where the first layer is trained, the results of the first layer are then re-trained, and the results of the second layer calculation are made into the third layer count, the mean results on the calculation of the third layer become the result of learning Deep Belief Network then with the Template Matching algorithm, Deep Belief Network is assisted with the introduction of vehicle plates. In a study conducted using the DBN algorithm with the Template Matching method succeeded in recognizing a vehicle plate with a success percentage of 80% from 20 trials. The experiments carried out included plates that were not clearly seen. Failures that occur in the trials are generally due to under- or over-lighting on the vehicle plate.


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    Titel :

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


    Beteiligte:
    Michael, Michael (Autor:in) / Tanoto, Frenky (Autor:in) / Wibowo, Eric (Autor:in) / Lutan, Frenky (Autor:in) / Dharma, Abdi (Autor:in)

    Erscheinungsdatum :

    02.11.2019


    Anmerkungen:

    doi:10.30812/matrik.v19i1.475
    MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer; Vol 19 No 1 (2019); 27-36 ; 2476-9843 ; 1858-4144 ; 10.30812/matrik.v19i1



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    DDC:    629



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