Klasifikasi Jenis Citra Makanan Tunggal Berdasarkan Fitur Local Binary Patterns dan Hue Saturation Value Menggunakan Improved K-Nearest Neighbor - Sarah Najla Adha; Yuita Arum Sari; Randy Cahya Wihandika (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
To fulfill their basic needs, living things need foods. Foods that have poor quality can cause disease. To avoid this, digital image processing can be used to create a food classification system. Digital image processing is used to analyze features contained in food images. In this study, the feature used to classify the types of food images is a feature of color and texture. Color feature extraction is done by Hue Saturation Value (HSV) color space and texture features using the Local Binary Patterns (LBP) method. Classification is done by the Improved K-Nearest Neighbor (Improved K-NN) method. The test results for the k value indicate that the highest accuracy is obtained at 90.476% with the value of k = 1. When the feature used is only a color feature, the highest accuracy value is obtained at 90.476% with a value of k = 1. When the feature used is only a texture feature, the highest accuracy value is obtained 85.714% with a value of k = 1. The results of testing the classification method showed that the Improved K-NN method produced higher accuracy than the K-NN method with an average accuracy of 80.306%. So the best classification results are obtained by using a combination of color and texture features with the Improved K-NN classification method.