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All Journal Jurnal Buana Informatika Journal of ICT Research and Applications Jurnal Edukasi dan Penelitian Informatika (JEPIN) CESS (Journal of Computer Engineering, System and Science) Fountain of Informatics Journal Format : Jurnal Imiah Teknik Informatika JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Ilmiah FIFO CIRCUIT: Jurnal Ilmiah Pendidikan Teknik Elektro INOVTEK Polbeng - Seri Informatika JMM (Jurnal Masyarakat Mandiri) SINTECH (Science and Information Technology) Journal Jurnal Teknoinfo ILKOM Jurnal Ilmiah JUTIM (Jurnal Teknik Informatika Musirawas) J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) JURTEKSI IJISCS (International Journal Of Information System and Computer Science) Jurnal Riset Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CSRID (Computer Science Research and Its Development Journal) Jurnal Teknologi Komputer dan Sistem Informasi Jurnal Tekno Kompak Respati JTIKOM: Jurnal Teknik dan Sistem Komputer Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service J-SAKTI (Jurnal Sains Komputer dan Informatika) Insearch: Information System Research Journal JUSTIN (Jurnal Sistem dan Teknologi Informasi) International Journal of Informatics, Economics, Management and Science Bulletin of Informatics and Data Science Jurnal Informatika: Jurnal Pengembangan IT JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
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Journal : Jurnal Riset Informatika

CLASSIFICATION OF VEHICLE TYPES USING BACKPROPAGATION NEURAL NETWORKS WITH METRIC AND ECENTRICITY PARAMETERS Hendra Mayatopani; Rohmat Indra Borman; Wahyu Tisno Atmojo; Arisantoso Arisantoso
Jurnal Riset Informatika Vol 4 No 1 (2021): Period of December 2021
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (730.169 KB) | DOI: 10.34288/jri.v4i1.293

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.
CLASSIFICATION OF VEHICLE TYPES USING BACKPROPAGATION NEURAL NETWORKS WITH METRIC AND ECCENTRICITY PARAMETERS Hendra Mayatopani; Rohmat Indra Borman; Wahyu Tisno Atmojo; Arisantoso Arisantoso
Jurnal Riset Informatika Vol. 4 No. 1 (2021): December 2021
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (758.834 KB) | DOI: 10.34288/jri.v4i1.139

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.