Arnita, F. Marpaung, A. Widianto, M. Hidayat
This study aims to distinguish nine types of food based on the pixel intensity of the HSV image of the food. The segmentation method used is Hue, Saturation and Value (HSV) and compares its performance with RGB and RGB+HSV combinations. The segmentation method used is Hue, Saturation and Value (HSV) and compares its performance with RGB and RGB+HSV combinations. The data used in this study is image of green bean porridge, nuggets, brown rice, white rice, kale, sausage and rendang. The segmentation begins by converting 162 food photos into RGB images, then converting them into HSV images. The image segmentation results were then classified using the KNN algorithm and tested at k = 3, 5 and 7. This is indicated by the value of accuracy, precision. Recall, and F1-scores were 96%, 94%, 94% and 94%, respectively. © 2022 American Institute of Physics Inc.. All rights reserved.
Mathematic Department, Universitas Negeri Medan, Jalan Williem Iskandar Pasar V, Medan North Sumatera, Indonesia
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