Perbandingan Algoritma LBP dan Cascading LBP-GLCM untuk Ekstraksi Fitur pada Citra Beras

This study compares two image feature extraction algorithms: Gray Level Co-occurrence Matrix (GLCM) and a combination of Local Binary Pattern with GLCM (LBP GLCM), for rice image classification. The objective is to evaluate the effectiveness of both methods in generating features such as ASM, contrast, correlation, entropy, and energy, as well as to measure the computational time. The results show that the LBP GLCM algorithm significantly improves classification accuracy compared to pure GLCM, but requires 13-17 times longer computational time. While GLCM is more efficient in terms of time, its classification accuracy is relatively lower. These findings align with previous studies indicating that adding LBP to GLCM enhances classification performance. In conclusion, LBP GLCM is superior in accuracy, making it a better choice for applications that prioritize precise classification results. However, the trade-off in computational time should be considered, especially for applications requiring fast processing. These findings are relevant for further development in agriculture and image processing.Â