Implementasi K-Means Clustering dan Teknik Pengolahan Citra Dalam Klasifikasi Buah Kiwi dan Sawo

Abstrak

Kiwi and sawo are two types of fruits that share some similarities in skin color, but have significant differences in shape and texture. Despite their visual similarities in several aspects, a proper method is needed to differentiate and classify them automatically. One effective image processing technique for this purpose is K-Means clustering, which can be used to group objects based on visual feature similarities. This study aims to implement the K-Means clustering method to distinguish between kiwi and sapodilla fruits using digital images. The images of kiwi and sapodilla fruits, captured with a digital camera, were processed to extract important features such as color, shape, and texture. The results of applying K-Means clustering show that this method is effective in grouping the two fruits based on significant differences in shape and texture, despite their similarity in skin color. This research contributes to the development of automatic classification systems that can be applied in the agricultural industry, fruit scanning systems, and product quality monitoring