Modification of K-Nearest Neighbor Method with Normalized Euclidean Distance for Classification of Local Berastagi Orange Quality
DOI:
https://doi.org/10.62123/enigma.v2i2.60Keywords:
Classification, Berastagi Local Orange, K-Nearest Neighbor, Normalized Euclidean Distance, MatlabAbstract
Local Indonesian fruit is one example of Indonesia's natural wealth, one of which is the local Berastagi orange. Oranges are rich in vitamin C which is good for body health. Oranges tend to have a sour, fresh, and sweet taste. The vitamin C contained in oranges is 97.3 milligrams or equivalent to 163% of the nutritional adequacy rate. Not only Vitamin C, oranges also contain vitamin B6, antioxidants and fiber. Therefore, it is highly recommended to consume oranges every day because oranges can facilitate digestion, reduce the risk of diabetes, maintain healthy skin, and also maintain endurance. This study aims to apply the Classification and assessment of the quality of local oranges using the K-Nearest Neighbor (KNN) method modified with Normalized Euclidean distance to classify the quality of local Berastagi oranges based on the color of the fruit image. The research dataset was taken from 100 images of local Berastagi oranges, where the 100 images were divided into 2, namely, good oranges and bad oranges. The classification process for local Berastagi oranges uses the matlab application.
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