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A Good Performance of Convolutional Neural Network Based on AlexNet in Domestic Indonesian Car Types Classification
Advance Sustainable Science, Engineering and Technology (ASSET)
ISSN 2715-4211, 2715-4211
Published 2023-10-31
Publisher Universitas PGRI Semarang
Language eng
Abstract
Classification of car vehicle types has been carried out using CNN. There are weaknesses in the CNN algorithm so that it can be continued in the research we propose. This study aims to improve the previous accuracy by using the Alexnet architecture. To improve the results of the data set used we use threshold and brightness adjustment and data augmentation techniques for Reflection, Rotation, and Translation. Sample images with a resolution of 227x227x3 totaling 840 images used to represent 8 class types of cars, including Avanza, Fortuner, Freed, Inova, Pajero, Terios, Xenia, and Xpander. Alexnet with 10 epochs consisting of a total of 760 iterations, and validation is carried out every 30 iterations, the test results show that the use of the sgdm optimization function achieves a training accuracy of 99.74%, while the use of the adam optimization function produces an accuracy of 96.85%. This experiment shows the model's ability to classify the types of trainers after a success rate of 100%.
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25 authors ·
72 co-authorship links
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Metadata source: OAI-PMH (metadata jurnal) http://journal.upgris.ac.id/index.php/asset/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc)
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<dc:title xml:lang="en-US">A Good Performance of Convolutional Neural Network Based on AlexNet in Domestic Indonesian Car Types Classification</dc:title>
<dc:creator>Nabila, Ervira Aliya</dc:creator>
<dc:creator>Sari, Christy Atika</dc:creator>
<dc:creator>Rachmawanto, Eko Hari</dc:creator>
<dc:creator>Doheir, Mohamed</dc:creator>
<dc:subject xml:lang="en-US">literacy; local wisdom; natural and social science; wordwall</dc:subject>
<dc:description xml:lang="en-US">Classification of car vehicle types has been carried out using CNN. There are weaknesses in the CNN algorithm so that it can be continued in the research we propose. This study aims to improve the previous accuracy by using the Alexnet architecture. To improve the results of the data set used we use threshold and brightness adjustment and data augmentation techniques for Reflection, Rotation, and Translation. Sample images with a resolution of 227x227x3 totaling 840 images used to represent 8 class types of cars, including Avanza, Fortuner, Freed, Inova, Pajero, Terios, Xenia, and Xpander. Alexnet with 10 epochs consisting of a total of 760 iterations, and validation is carried out every 30 iterations, the test results show that the use of the sgdm optimization function achieves a training accuracy of 99.74%, while the use of the adam optimization function produces an accuracy of 96.85%. This experiment shows the model's ability to classify the types of trainers after a success rate of 100%.</dc:description>
<dc:publisher xml:lang="en-US">Universitas PGRI Semarang</dc:publisher>
<dc:contributor xml:lang="en-US">Magister Pendidikan Fisika, FKIP Universitas Sriwijaya</dc:contributor>
<dc:date>2023-10-31</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
<dc:format>application/pdf</dc:format>
<dc:identifier>https://journal.upgris.ac.id/index.php/asset/article/view/16854</dc:identifier>
<dc:identifier>10.26877/asset.v5i3.16854</dc:identifier>
<dc:source xml:lang="en-US">Advance Sustainable Science, Engineering and Technology; Vol 5, No 3 (2023): August-October; 0230302</dc:source>
<dc:source>2715-4211</dc:source>
<dc:source>2715-4211</dc:source>
<dc:language>eng</dc:language>
<dc:relation>https://journal.upgris.ac.id/index.php/asset/article/view/16854/pdf</dc:relation>
<dc:rights xml:lang="en-US">Copyright (c) 2023 Advance Sustainable Science Engineering and Technology</dc:rights>
<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>
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APA 7
Nabila, E. A., Sari, C. A., Rachmawanto, E. H., & Doheir, M. (2023). A Good Performance of Convolutional Neural Network Based on AlexNet in Domestic Indonesian Car Types Classification. Advance Sustainable Science, Engineering and Technology (ASSET), 5(3). https://doi.org/10.26877/asset.v5i3.16854
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IEEE
E. A. Nabila, C. A. Sari, E. H. Rachmawanto, and M. Doheir, “A Good Performance of Convolutional Neural Network Based on AlexNet in Domestic Indonesian Car Types Classification,” Advance Sustainable Science, Engineering and Technology (ASSET), vol. 5, no. 3, 2023, doi: 10.26877/asset.v5i3.16854.
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