Feri Wibowo, Agus Harjoko, Hindayati Mustafidah, Sriyanto, Alvanita
The quality identification process using a visual human eyes has its limitations, it requires more power to sort out. Furthermore, it has different levels of human perception in identifying the quality. This research develops digital image processing programs and artificial neural network for the quality classification of papaya Calina IPB-9 into three quality classes: Super, A, and B classes. Shape feature is extracted consistsing of compactness and roundness. Texture feature which is extracted consisting of the value of energy, entropy, contras, homogeneity, inverse difference moment, variance, and dissimilarity obtained by GLCM, another texture feature sought a feature LBP. Color feature is extracted consisting of the mean red, greeen, blue, hue, saturation, and value. These features serve as input to the training backpropagation neural network. The recognition process results shows the features of energy and entropy can distinguish quality classes of the papaya with the best accuracy rate that is equal to 87%. © 2018 SERSC Australia.
Universitas Muhammadiyah Purwokerto, Indonesia; Universitas Gadjah Mada, Yogyakarta, Indonesia; Universitas Negeri Yogyakarta, Indonesia
