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