CLASSIFICATION OF VEHICLE TYPES USING BACKPROPAGATION NEURAL NETWORKS WITH METRIC AND ECCENTRICITY PARAMETERS

Hendra  Mayatopani; Rohmat Indra Borman; Wahyu Tisno Atmojo; Arisantoso Arisantoso

doi:10.34288/jri.v4i1.139

Authors

Hendra Mayatopani Universitas Pradita
Rohmat Indra Borman Universitas Teknokrat Indonesia
Wahyu Tisno Atmojo Universitas Pradita
Arisantoso Arisantoso Sekolah Tinggi Teknologi Informasi NIIT Jakarta

(*) Corresponding Author

DOI:

https://doi.org/10.34288/jri.v4i1.139

Keywords:

backpropagation, metrics, eccentricity,, artificial neural network, vehicle classification

Abstract

One of the efforts to break down traffic jams is to establish special lanes that can be passed by two, four, or more wheeled vehicles. By being able to recognize the type of vehicle can reduce congestion. Citran based vehicle classification helps in providing information about the vehicle type. This study aims to classify the type of vehicle using a backpropagation neural network algorithm. The vehicle image can be recognized based on its shape, then the backpropagation neural network algorithm will be supported by metric and eccentricity parameters to perform feature extraction. Then from the results of feature extraction with metric parameters and eccentricity, the object will be classified using a backpropagation neural network algorithm. The test results show an accuracy of 87.5%. This shows the algorithm can perform classification well.

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