Pengenalan Citra Jenis Makanan Menggunakan Klasifikasi Naive Bayes Dengan Ekstraksi Fitur Hue Saturation Intensity Color Moments Dan Morphological Shape Descriptors - Ian Lord Perdana; Yuita Arum Sari; Sutrisno Sutrisno (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
The process of determining some kind of food become important because it will determine the food that will be processed in the system that recorded the food for health and diet purpose. The process of determining food consist of preprocessing process and then changing the color model of the image from RGB to HSI. The next process is color feature extraction with Color Moment method that will generate the mean feature, standard deviation feature, and skewness feature from every color channel. Then, for shape feature extraction will using Morphological Shape Descriptors that will generate the length feature, width feature, diameter feature, and aspect ratio feature from the image. After the feature get extracted from the image, do the classification process with Naive Bayes Method with the help of LogSumExp for the probability calculation. The result in the testing of the effect of testing data generate 78% accuracy value when using 100 testing data. The result in the testing the effect of image dimension generate 81% accuracy value when using 300x300 pixel image for testing. The result in the testing the effect of number feature used generate 83% accuracy value when using feature from Color Moment only. The conclusion is, the feature extraction Color Moment and Morphological Shape Descriptors with Naive Bayes classification can be used to determine the kind of some food.