Penerapan LightGBM Dengan Kombinasi Ekstraksi Fitur HSV, GLCM dan HOG Untuk Klasifikasi Bunga - Sedik, Stevani Gabriella Ayuk; Tentua, Meilany Nonsi (Vortex, 2026)
This study applies the LightGBM algorithm with a combination of GLCM, HOG, and HSV feature extraction for flower image classification. The dataset used consists of five types of flowers, namely Sunflower, Rose, Tulip, Dandelion, and Daisy, with a total of 4,242 images. Each image undergoes preprocessing and feature extraction of texture, shape, and color before being trained using LightGBM. The results show that the proposed model achieves an accuracy above 70% in distinguishing the five flower classes. This study provides evidence that the combination of GLCM, HOG, and HSV with LightGBM is able to improve classification performance and can serve as a reference for further research in the field of digital image processing