Klasifikasi Tiga Jenis Macan Tutul Berbasis Pengolahan Citra Digital Menggunakan Kombinasi Fitur RGB, GLCM, PCA dan SVM

Abstract

Classification of leopard species based on digital images is one of the applications of image processing and data mining that can support the automatic identification of wildlife. This study aims to develop a classification model for three leopard species, namely African Leopard, Clouded Leopard, and Snow Leopard, based on the color and texture characteristics of images. The research process includes image pre-processing, color feature extraction using the Red, Green, Blue (RGB) channels, and texture feature extraction using the Gray-Level Co-occurrence Matrix (GLCM), which produces contrast, correlation, energy, and homogeneity features. The extracted features are then reduced using Principal Component Analysis (PCA) while retaining 95% of the information variance, before being classified using Support Vector Machine (SVM) with the multi-class Error-Correcting Output Codes (ECOC) approach. Evaluation is conducted using three data-splitting scenarios for training and testing sets. The results show that the scenario with 70% training data and 30% testing data achieves the highest accuracy of 87.06%, with class accuracies of 87.5% for African Leopard, 79.3% for Clouded Leopard, and 94.6% for Snow Leopard. These results indicate that the combination of RGB, GLCM, PCA, and SVM can classify the three leopard species with reasonably good performance.