KLASIFIKASI KENDARAAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DENGAN FITUR HISTOGRAM OF ORIENTED GRADIENT

ICHWAN, RIDHO and Sutarno, Sutarno (2019) KLASIFIKASI KENDARAAN MENGGUNAKAN METODE SUPPORT VECTOR MACHINE DENGAN FITUR HISTOGRAM OF ORIENTED GRADIENT. Undergraduate thesis, Sriwijaya University.

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Abstract

The vehicle is a tool needed by humans in running their daily activities. With many types of vehicles, a vehicle classification system is needed based on the shape consisting of two-wheeled motorcycles, sedan cars, minibuses and trucks. Vehicle classification system is one method of analyzing the type of vehicle based on the shape and can be implemented in the field of transportation to recognize the type of vehicle so that it can classify vehicles based on shape. Vehicle feature extraction using histogram of oriented gradient (HOG) and support vector machine (SVM) is used to classify vehicles based on the shape obtained. Sampling data using a smartphone camera with a distance of ± 5 meters from a vehicle with a height of 1.5 meters. The results of the accuracy of the system in detecting vehicles using a support vector machine (SVM) based on the extraction of histogram of oriented gradient (HOG) features with 20 test samples, obtained an accuracy of 95% well classified.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Klasifikasi Kendaraan, Histogram of oriented gradient, support vector machine
Subjects: T Technology > T Technology (General) > T10.5-11.9 Communication of technical information
T Technology > T Technology (General) > T58.5-58.64 Information technology > T58.5 General works Management information systems Cf. HD30.213 Industrial management Cf. HF5549.5.C6+ Communication in personnel management Cf. TS158.6 Automatic data collection systems (Production control)
T Technology > T Technology (General) > T58.5-58.64 Information technology
Divisions: Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User: Mr. Ridho Ichwan
Date Deposited: 13 Sep 2019 06:34
Last Modified: 13 Sep 2019 06:34
URI: http://repository.unsri.ac.id/id/eprint/7336

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