Klasifikasi Citra Makanan Menggunakan HSV Color Moment dan Local Binary Pattern dengan Naive Bayes Classifier - Karunia Ayuningsih; Yuita Arum Sari; Putra Pandu Adikara (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Food is a basic need that must be fulfilled in human life. Eating habits can lead to good and bad habits. Bad eating habits can cause various diseases. Komunikasi, informasi, dan edukasi (KIE) can provide education on eating habits. Food has a variety of types, it is necessary to recognize the type of food to make it easier to identify good types of food. The purpose of this study is to be able to provide education to recognize the types of food. The process begins with image identification using pre-processing to separate between food objects and background. On top of that, using the Hue Saturation Value (HSV) color extraction feature consists of the feature Mean, the Standard Deviation, and the Skewness. Then is the use of the Local Binary Pattern (LBP) texture feature extraction produce feature extraction uses gray scales in the histogram. The results of feature extraction from each image are then carried out using the Naive Bayes Classifier classification. Based on the test results, the use of only the HSV method produces a 65% accuracy value. Meanwhile, the use the LBP method, get a 60% accuracy value. In addition, the results of tests that have been carried out using the HSV method produce an accuracy of 65% and the LBP method produces an accuracy of 60%.