Seleksi Fitur Information Gain Pada Temu Kembali Citra Jenis Makanan Menggunakan Dominant Color Descriptor Dan Gray Level Co-occurence Matrix - Sulaiman Triarjo; Yuita Arum Sari; Sigit Adinugroho (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Nutritional information on social media is supported by image of food being reviewed. It requires hard work to explore similar foods that have almost same nutrition. Therefore an information search system is needed to speed up the information search process. This research has been conducted to be able to search for similar informations based on a query in form of image. It uses Dominant Color Descriptor method for color feature extraction and Gray Level Co-occurence Matrix method for texture feature extraction and information gain selection feature to select texture features. The data used were 29 types of food imaged with total is 435 images which each type has 15 images. Testing is done by comparing the performance of calculation of Euclidean distance, Chebyshev distance, and Manhattan distance for texture feature and Quadratic distance and Yang distance for color feature. The evaluation uses MAP value, test result using only the texture feature obtained MAP value of 0,5542 using Euclidean distance and without feature selection. The test result using only color feature obtained MAP value of 0,7488 when using Yang distance. And testing using color feature and texture feature obtained a value of 0,7118 by using Manhattan distance and Yang distance with 10 features. In this research, the use of DCD was more effective than GLCM by producing higher MAP value.