Seleksi Fitur Information Gain pada Klasifikasi Citra Makanan Menggunakan Ekstraksi Fitur Haralick dan YUV Color Moment - Devinta Setyaningtyas Atmaja; Yuita Arum Sari; Randy Cahya Wihandika (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Food classification is a classic problem that still becomes an interest for many researchers. Several studies have been conducted using only one type of food, which is fruit as the object of the classification. This research was conducted to improve the previous ones. This study uses five types of single food as its object. The method used is color feature extraction using YUV Color Moment, texture feature extraction using Haralick, and feature selection using Information Gain. The classification algorithm is K-Nearest Neighbor (KNN). The highest accuracy obtained is 94.26% obtained from the combined features of the two selected feature extraction methods. From these results, it can be concluded that the application of a combination of feature extraction methods, namely color and texture, and feature selection method greatly influence the food image classification process.