Klasifikasi Citra Makanan Menggunakan Algoritme Learning Vector Quantization Berdasarkan Ekstraksi Fitur Color Histogram dan Gray Level Co-occurrence Matrix - Sarah Yuli Evangelista Simarmata; Yuita Arum Sari; Sigit Adinugroho (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2019)
Many photos of food we see on social media, but we forget and don't even know the name of the food. Humans ability to recognize and identify is also subjective to external such as fatigue, prejudice and etc. Computers can help by build a system that can recognize and identify food through images. Researches have been conducted that the process of automatically identifying and classifying using computer can save more time compared to identify manually. Food image has different colors and textures. The color feature extraction method used in this research is Color Histogram and for the texture feature extraction is Gray level co-occurrence matrix (GLCM). The classification algorithm used is Learning Vector Quantization (LVQ) with the best parameters that can be used are learning rate (Î±) 0.1, decreament learning rate 0.1, maximum epoch 2, minimum learning rate 0.01 and gives accuracy that is equal to 53,33%. The test gives 53.33% accuracy for using color and texture extraction. The use of color feature extraction only gives the highest accuracy that is equal to 67.00%, and the use of texture feature extraction only gives accuracy that is equal to 53.33%. From the results, concluded that LVQ algorithm based on Color Histogram feature extraction and GLCM can be used to classify food image but can not give a perfect accuracy.