Seleksi Fitur Information Gain Pada Klasifikasi Citra Makanan Menggunakan Hue Saturation Value dan Gray Level Co-Occurrence Matrix - Frisma Yessy Nabella; Yuita Arum Sari; Randy Cahya Wihandika (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Choosing food has become a challenge for those who are presented with new food choices. Classification is important for those who have a strict diet regarding food that they consume. Food selection is essential for those who are visually impaired to identify food items. The classification process in this research is initiated with the pre-processing of the image, resulting in a segmented image which is then continued with feature extraction where Hue Saturation Value (HSV) for color extraction and Gray Level Co-Occurence Matrix (GLCM) for texture features. Based on features that have been extracted the next step is to gather relevant features using Information gain to reduce the workload. The last process is classification, using K-Nearest Neighbor. Accuracy results are 95,24% at best using only HSV with k=1 for feature selection. A combination of HSV and GLCM using Information gain results in a accuracy from 57,14% to 87,61%. This also applies to only using GLCM with information gain that raises the accuracy from 57,14% to. 74,28%. With the previous statement taken into consideration, Information Gain as a feature selection method increases accuracy with a significant amount and is able to obtain relevant feature if the list of features is abundant. If there are only a few features used, the accuracy increment is not that significant but it decreases the workload of the system.