Klasterisasi Citra Kucing dan Anjing Menggunakan Algoritma K-Means untuk Identifikasi Hewan Peliharaan Berbasis Visi Komputer - Charisman Fajri Saputra; Intan Utnasari (GLOBAL SCIENTS PUBLISHER, 2026)
Automatic identification of pets, especially cats and dogs, is a challenge in the field of computer vision. K-Means Clustering can be utilized to classify cat and dog images based on visual features such as color, shape, and texture. This study aims to analyze the effectiveness of the K-Means algorithm in distinguishing between these two animal species. The methodology includes dataset collection consisting of 8 images (4 images of cats and 4 images of dogs), image preprocessing through normalization and color feature extraction, and K-Means implementation with K = 2 clusters. Evaluation is conducted using the Silhouette Score to measure clustering quality. The results indicate that K-Means achieves a classification accuracy of 100% due to the small dataset size and the distinct visual differences between the two animals. The findings suggest that K-Means can serve as an initial approach for automatic pet identification, but further validation with larger and more diverse datasets is needed. Improvements through advanced clustering techniques or integration with deep learning models are also recommended for higher accuracy.