Butterfly Feature Extraction Using HSV, Lacunarity, and CNN - Rahayu, Putri Nur; Sukarno, Friska Intan; Yuniati, R. A. Norromadani; Rakhmadi, Ardhon; Augustino, Immanuel Freddy (Journal of Artificial Intelligence and Software Engineering, 2025)
This study aims to extract the morphological features of butterflies using the HSV (Hue, Saturation, Value) and lacunarity. The HSV method is used to obtain color information from butterfly images. lacunarity is used to extract texture characteristic to enhance the visual representation of the object. These extracted features are used as input for the processing of classification using algorithm of Convolution Neural Network (CNN). Based on the experimental result, the classification has accuracy 70%. This accuracy indicates that the combination of HSV and lacunarity methods is sufficiently effective in describing of the visual butterflies features for automatic classification.