Klasifikasi Ngengat dan Kupu-Kupu Menggunakan Metode GLCM dan Support Vector Machine
Abstract
Butterflies and moths are two types of insects that share similarities in their appearance and physical characteristics. Both insects exhibit a variety of colors, patterns, and body shapes that are often difficult to distinguish. This research aims to classify butterflies and moths using feature extraction from the Gray-Level Co-occurrence Matrix. The feature extraction process involves extracting values such as correlation, homogeneity, contrast, and energy from angles of 0°, 45°, 90°, and 135° in each butterfly and moth image. Furthermore, the Support Vector Machine method is used for classification. The research results indicate that using feature extraction from the Gray-Level Co-occurrence Matrix and the Support Vector Machine method can achieve an accuracy of 68.11%, with precision, recall, and F1-Score values of 70.0%, 68.0%, and 68.0%, respectively.
Keywords: Classification, Gray-Level Co-occurrence Matrix, Feature extraction, Support Vector Machine, Butterflies, Moths
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