Implementasi Algoritme K-Means Sebagai Metode Segmentasi Citra Dalam Identifikasi Penyakit Daun Jeruk - Falih Gozi Febrinanto; Candra Dewi; Anang Tri Wiratno (Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya, 2018)
One of the factors that causes poor quality of citrus crops is the disease which attacks the leaves. The development of information technology in digital image processing field allows to identify the citrus leaf disease automatically. This research identifies the citrus leaf disease includes Downy Mildew, Cendawan Jelaga, and CVPD (Citrus Vein Phloem Degeneration). The identification process of citrus leaf disease begins with resizing to equalize image size and rescaling to adjust the image brightness. Next, converting RGB to L*a*b* color space. After converting the color space, the results of the conversion is used as an input to image segmentation using K-Means algorithm. There are two segmentation parts, namely leaf segmentation and disease segmentation. After segmentation process, the results of disease segmentation are classified by using K-Nearest Neighbor (K-NN) algorithm on the train data to knows the class of their diseases. Tests conducted on this research are testing the value of Scale Factor, optimal cluster value, and optimal K value. Based on the three conducted tests, it recommends that the Scale Factor value is 1.1, the optimal cluster value on leaf segmentation is 2, the optimal cluster value on the segmentation of disease is 9, and the optimal K value is 4. The highest accuracy that obtained for disease identification in this research is 90.83%.