Record Details

MODEL CNN LENET DALAM PENGENALAN JENIS GOLONGAN KENDARAAN PADA JALAN TOL

Jurnal Teknika

View Archive Info
 
 
Field Value
 
Title MODEL CNN LENET DALAM PENGENALAN JENIS GOLONGAN KENDARAAN PADA JALAN TOL
 
Creator Pramana, Anggay Luri
Setyati, Endang
Kristian, Yosi
 
Subject Teknik Informatika
CNN, LeNet, Relu Activation, Transportation System, Vehicle Type Classification
Klasifikasi, Data Mining
 
Description Research in the field of transportation, especially vehicle classification with various methods, is a widely developed field of study. Vehicles can be categorized by shape, dimension, logo, and  type. The vehicle dataset is also not difficult to find because it is general in nature. Based on the research that has been done, the introduction of group types based on the number of axles with CNN, the dataset is not yet available to the public. In this paper, we discuss the introduction of the types of groups using the Convolutional Neural Network method. The architecture used is the LeNet model. The trial scenario is carried out in 4 stages, namely 25 epochs, 50 epochs, 75 epochs and 100 epochs. Based on the test results, the accuracy obtained continues to increase at 50 epochs and 100 epochs iterations. Starting from an accuracy of 82%, 94% to the highest accuracy of 95%. Likewise in the prediction the data has increased from 80%, 85% to the highest accuracy that can be 86%. From 50 epochs to 75 epochs, the accuracy of both training and testing has decreased.
 
Publisher Universitas Islam Lamongan
 
Contributor —
 
Date 2020-09-20
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
—
—
Model CNN
 
Format application/pdf
 
Identifier https://jurnalteknik.unisla.ac.id/index.php/teknika/article/view/469
10.30736/jt.v13i2.469
 
Source Teknika; Vol 12, No 2 (2020): Bersinergi untuk kemajuan dan perkembangan teknologi bangsa indonesia; 65-69
Jurnal Teknika; Vol 12, No 2 (2020): Bersinergi untuk kemajuan dan perkembangan teknologi bangsa indonesia; 65-69
2620-4770
2085-0859
10.30736/jt.v13i2
 
Language ind
 
Relation https://jurnalteknik.unisla.ac.id/index.php/teknika/article/view/469/334
 
Rights ##submission.copyrightStatement##
https://creativecommons.org/licenses/by/4.0