ACADSTAFF UGM

CREATION
Title : Deteksi Ketersediaan Slot Parkir Berbasis Pengolahan Citra Digital Menggunakan Metode Histogram of Oriented Gradients dan Support Vector Machine
Author :

Ika Candradewi, S.Si., M.Cs. (2)

Date : 2017
Keyword : feature extraction, histogram of oriented gradient, classification, support vector machine feature extraction, histogram of oriented gradient, classification, support vector machine
Abstract : This research aims to implement method based on digital image processing to inform the status of parking slots at the car parking area by using a feature extraction HOG (Histogram of Oriented Gradients) method in every region of the parking area. Feature extraction results are classified using SVM (Support Vector Machine) by comparing the Linear, RBF (Radial Basis Function), Poly, and Sigmoid kernels. SVM classification results were analyzed using the confusion matrix with accuracy, specificity, sensitivity, and precision parameters. In terms of accuracy, system obtained with Linear kernel in sunny conditions shows 98.0% accuracy; rainy 98.8% accuracy; cloudy 99.2% accuracy. Obtained accuracy using Poly kernel test in sunny conditions shows 99.2%; rainy 98.9%; cloudy 99.4%. Obtained accuracy using RBF kernel in sunny conditions shows 97.9%; rainy 98.7%; cloudy 99.6%. In terms of accuracy using additional data testing obtained with Linear kernel shows accuracy of 97.7%; RBF kernel 97.9% accuracy; Poly kernel 97.4% accuracy. Sigmoid kernel testing can’t be used because the optimal model did not obtained by using default grid.
Group of Knowledge : Ilmu Komputer
Original Language : Bahasa Indonesia
Level : Nasional
Status :
Published
Document
No Title Document Type Action