Analisis Distribusi Intensitas RGB Citra Digital untuk Klasifikasi Kualitas Biji Jagung menggunakan Jaringan Syaraf Tiruan Muhammad Arief Bustomi, Ahmad Zaki Dzulfikar

Abstract

The research and manufacture of computer programs based backpropagation neural network which aims to classify the quality of corn seeds based on RGB intensity distribution pattern of digital image of the corn seeds have been done. In this research, the quality of corn seeds classified into 4 groups, namely rotten seeds, moldy seeds, normal seeds and damaged seeds. The number of samples used is 120 samples for training and 80 samples for testing. The order of the stages of the research are as follows : filtering the digital image with the median filter, equalization with adaptive histogram, extracting RGB color index for the three RGB color intensity, and calculating the mean and standard deviation for each of the RGB color index. Furthermore, from the pattern of the mean and standard deviation of the three RGB color index can be used to identify the quality of corn seeds using backpropagation neural network method. In this research, the neural network using the log-sigmoid activation function and can recognize patterns optimally when used 1500 iterations, 500 neurons, 4 hidden layer, the output layer 4, and a learning rate of 0.01. The results showed that the neural network which has been made apparently has an average accuracy rate of 100% on the training process and amounted to 73.75% on the testing process.