Classification of Beef and Pork Images Based on Color Features and Pseudo Nearest Neighbor Rule - Baiti, Ahmad Awaluddin; Fachrie, Muhammad; Diwandari, Saucha (Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY, 2023)
This research is motivated by the need for halal foods in Muslim society with the purpose of avoiding non-halal foods, such as pork, that are sold in the market. Although beef and pork basically have different characteristics, not all Muslims know the differences. Moreover, people nowadays sell beef mixed with pork to obtain more profits. Hence, this paper proposed the implementation of the Pseudo-Nearest Neighbor Rule (PNNR) in classifying images of beef and pork slices based on color features. Based on the image dataset that has been collected, the very significant difference that can be identified visually between beef and pork is the color. The color features were extracted from the image using a color histogram from two different color channels, RGB and HSV. As the result, PNNR that used color features from the RGB channel achieved up to 87.43% accuracy, while using the HSV channel, it can reach up to 93.78% of accuracy. Additionally, this paper evaluates the stability of the proposed method by assessing the variance of classification accuracy across different values of k. It is also noticed that PNNR's performance is relatively consistent for various values of k compared to the traditional kNN algorithm.