Klasifikasi Buah Jeruk Menggunakan Metode KNN Berdasarkan Fitur RGB dan HSV
Abstract
Classification of citrus fruit freshness is an important challenge in the agricultural industry to improve efficiency and product quality. This study aims to classify fresh and rotten oranges based on RGB (Red, Green, Blue) and HSV (Hue, Saturation, Value) color feature extraction using the K-Nearest Neighbor (KNN) algorithm. The dataset used includes 146 data for training and 88 data for testing. The KNN algorithm was tested with k values varying between 1 and 7. The purpose of this study is to make it easier for farmers to classify citrus fruits, so that they can save time and increase the accuracy of fruit quality assessment. The test results show that this method is able to classify fresh and rotten citrus fruits with an accuracy of 88.95%. This study proves that the RGB and HSV-based classification system using KNN can be relied on to assist the citrus fruit quality assessment process.
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