<![CDATA[Food is a primary need to help individuals in executing their daily activities. The nutritional value provided by certain food items affects one's performance in executing their daily activities. Individuals need to be assisted in identifying what food items are nutritious and those that are not, hence a classification algorithm is made for this task. Computer vision can be utilized to classify food items based on analyzing certain features. This research uses color and texture features to classify food items that are in images. Color feature extraction utilizes Color Moments (CM) using a Red, Green, and Blue (RGB) color channel, while Local Binary Patterns (LBP) is utilized for texture feature extraction. The k-Nearest Neighbors (k-NN) is used for the classification process. The digital images, from both the testing and training groups, will be preprocessed whose color features will be extracted with CM and the texture features with LBP. The extracted features will then be saved in a database, which will decrease computing time during the classification time. Varying the values of k in the k-nearest neighbors algorithm during testing and combinatios of features used, showed that the highest value for f1-score during evaluations was 0,89 when the value of k=1 and when only the color features from using color moments were used. Therefore the classification algorithm works efficiently on the dataset used in the research if only color features were used using k-NN as the classification algorithm.]]>
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