Klasifikasi Ngengat dan Kupu-Kupu Menggunakan Metode GLCM dan Support Vector Machine - I Dewa Made Mardana; Luh Gede Astuti (Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University, 2024)
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 GrayLevel 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.