Penerapan Algoritma Support Vector Machine Dan Naive Bayes Untuk Klasifikasi Jenis Mobil Terlaris Berdasarkan Produksi Di Indonesia

Paul, H.; Wiguna, Anggri Sartika; Santoso, Heri

doi:10.36040/jati.v7i1.5555

Cited by 1 publication

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“…Consequently, the vehicles traversing Indonesian roads represent a fusion of domestically manufactured cars and imported ones originating from diverse nations. This phenomenon has led to the growth of industries that not only cater to a substantial labor force but also drive economic expansion [1], [8]. Vehicle classification stands as a pivotal aspect within modern traffic supervision and regulation systems.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Augmentation-Based Resnet50 for Car Brand Classification

Sugiarto,

Soeleman,

Pujiono

2023

JAIS

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This research focuses on car classification and the use of the ResNet-50 neural network architecture to improve the accuracy and reliability of car detection systems. Indonesia, as one of the countries with high daily mobility, has a majority of the population using cars as the main mode of transportation. Along with the increasing use of cars in Indonesia, many automotive industries have built factories in this country, so the cars used are either local or imported. The importance of car classification in traffic management is a major concern, and vehicle make and model recognition plays an important role in traffic monitoring. This study uses the Vehicle images dataset which contains high-resolution images of cars taken from the highway with varying viewing angles and frame rates. This data is used to analyze the best- selling car brands and build car classifications based on output or categories that consumers are interested in. Digital image processing methods, machine learning, and artificial neural networks are used in the development of automatic and real-time car detection systems.The ResNet-50 architecture was chosen because of its ability to overcome performance degradation problems and study complex and abstract features from car images. Residual blocks in the ResNet architecture allow a direct flow of information from the input layer to the output layer, overcoming the performance degradation problem common in neural networks. In this paper, we explain the basic concepts of ResNet-50 in car detection and popular techniques such as optimization, augmentation, and learning rate to improve performance and accuracy. in this study, it is proved that ResNet has a fairly high accuracy of 95%, 92% precision, 93% recall, and 92% F1-Score.

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Section: Introductionmentioning

confidence: 99%