A Implementasi K-Means Clustering dalam Segmentasi Citra Hewan pada Kucing, Kambing, dan Burung - Delvi, Syerlin Aprilia; Ramadhanu, Agung (Jurnal Sains dan Teknologi (JSIT), 2025)
Image segmentation is one of the most important challenges in digital image processing because it determines the successof separating the main object from the background so that visual information can be further analyzed. The problem ariseswhen the object has complex color, texture, and shape characteristics, as in animal images that often have color patternssimilar to their surroundings, making object boundaries difficult to distinguish clearly. This study aims to apply the KMeans Clustering method in the process of animal image segmentation—specifically for cats, goats, and birds—and toevaluate its effectiveness in identifying and separating the main object from the background. The method used is the KMeans Clustering algorithm, an unsupervised learning technique that groups image pixels based on color similarity in theRGB color space through an iterative process until centroid stability is achieved and clusters representing different imageregions are formed. The results show that the K-Means method can produce good segmentation performance for imageswith uniform lighting and simple backgrounds but experiences a decrease in accuracy when the object’s color is similar toits environment. Overall, this algorithm is effective, simple, and can serve as a foundation for developing automatedanimal image identification and classification systems