KLASIFIKASI TUTUPAN LAHAN SAWAH DAN KELAPA SAWIT MENGGUNAKAN GLCM DAN K-NEAREST NEIGHBOR PADA CITRA UDARA
Abstract
This study aims to automatically classify rice field and oil palm land cover based on aerial imagery by utilizing the Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction and the K-Nearest Neighbor (KNN) algorithm as the classification method. The dataset consists of 130 training images and 111 test images. The images were processed through cropping and grayscale conversion, followed by texture feature extraction including contrast, correlation, energy, and homogeneity. These features serve as the foundation for distinguishing the unique texture patterns of each land type.
The test results show that the K parameter in KNN significantly affects the classification accuracy, with K=7 achieving the best result of 97.30%. Evaluation using a confusion matrix reinforces the effectiveness of the method in distinguishing the two land cover classes. The combination of GLCM and KNN proves to be both efficient and accurate, with great potential to be applied in automated mapping and monitoring systems, particularly in agricultural and plantation contexts.
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