ALGORITMA KNN UNTUK KLASIFIKASI KEMATANGAN BUAH APEL BERDASARKAN TEKSTUR
Abstract An apple is a fruit that is widely planted in mountainous or cold regions, for example in Malang. Apples are fruits that have many colors, there are green, yellow, and red colors. With a variety of colors that make consumers feel confused whether the apples to be eaten are sweet or sour. Because almost most consumers do not know the type of apple that will be purchased. Sometimes the type of apple that will be purchased is green, but because it does not know ripe or raw, it is wrong to choose. In order to help consumers in knowing the level of maturity of apple again, a system was made. With the aim to be able to classify the level of maturity of the apple again. So that the system created will display information whether the apple is more ripe or raw. The system will process the image of the apple again and take the GLCM texture features (intensity, contrast, energy, smoothness, entropy, skewness). And the process of determining fruit maturity using the KNN method. The system was built using the matlab tool, with 200 datasets, consisting of 130 training datasets, and 70 testing datasets. In applying the KNN algorithm to determine the maturity level of apples, the accuracy results are 51.4%. With output data that is not in accordance with the target number of 34 data and according to the target number of 36 data.