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

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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
Penulis: Mulia Octavia, Jesslyn K, Gasim Gasim
Kode Jurnal: jptinformatikadd160957

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Jp Teknik Informatika dd 2016