A Good Performance of Convolutional Neural Network Based on AlexNet in Domestic Indonesian Car Types Classification

Ervira Aliya Nabila, Christy Atika Sari, Eko Hari Rachmawanto, Mohamed Doheir


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%.


Alexnet, Classification, Cars, CNN, Image Processing, Car

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DOI: https://doi.org/10.26877/asset.v5i3.16854


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Advance Sustainable Science, Engineering and Technology (ASSET)

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