Klasifikasi Varietas Cabai Berdasarkan Morfologi Daun Menggunakan Backpropagation Neural Network
Abstract: Compared with other
methods of classifiers such as cellular and molecular biological methods, using
the image of the leaves become the first choice in the classification of
plants. The leaves can be characterized by shape, color, and texture; The
leaves can have a color that varies depending on the season and geographical
location. In addition, the same plant species also can have different leaf
shapes. In this study, the morphological features of leaves used to identify
varieties of pepper plants. The method used to perform feature extraction is a
moment invariant and basic geometric features. For the process of recognition
based on the features that have been extracted, used neural network methods
with backpropagation learning algorithm. From the neural-network training, the
best accuracy in classifying varieties of chili with minimum error 0.001 by
providing learning rate 0.1, momentum of 0.7, and 15 neurons in the hidden
layer foreach of various feature. To conduct cross-validation testing with
k-fold tehcnique, obtained classification accuracy to be range of 80.75%±0.09%
with k=4.
Penulis: Kharis Syaban, Agus
Harjoko
Kode Jurnal: jptinformatikadd160306
