PERBANDINGAN TINGKAT AKURASI JENIS CITRA KEABUAN , HSV, DAN L*a*b* PADA IDENTIFIKASI JENIS BUAH PIR

Abstract

Image processing has been commonly used in automatic object identification. These are some methods that can be used for automatic object identification, such as LVQ, K-NN, SVM, and Neural Network. This research specifically bring out the topic about the level accuracy comparison in identification of pear variety using grayscale, HSV, and L*a*b* images in aim to get the best image type for pear image identification using neural network. The feature are gray level co-occurrence matrix feature (energy, entropy, homogeneity, and contrast) from canny edge detection’s image and also color feature. Based on image examination result, grayscale reached its best accuracy for 90% on MSE 1e-10 with 10 hidden layer neurons, HSV reached its best accuracy for 100% on MSE 1e-5 with 20 hidden layer neurons, L*a*b* reached its best accuracy for 100% on MSE 1e-5 with 15 hidden layer neurons. HSV and L*a*b* give the better accuracy for pear variety image identification than grayscale.

Keyword:Image Processing, Pear, Neural Network, Identification, Gray Level Co-occurrence Matrix, Canny, Color.