Performance analysis of local binary pattern and k-nearest neighbor on image classification of fingers leaves

The K-Nearest Neighbor (KNN) method is often used by researchers for the classification process because it has a relatively great level of accuracy, however it also has a weakness which is sensitive of the noises. This research is aims to introduce an object recognition (identification) system of fingers leaves by classified using the KNN method. To resolves the weaknesses of the KNN method, the researcher has used the Local Binary Pattern (LBP) method to extract features of the leaves. For the comparison in feature extraction, the researcher has used the Gray Level Co-Occurrence Matrix (GLCM) method. The data that were used on this research are papaya leaves and chaya leaves (with the labels such as good and damage forms). In this research, an experimental design has been carried out that was differentiated by according to the comparison (of ratio) between training data and testing data (NI/Np), there were 90 training data and 45 testing data, where the feature extraction method used the 10 of features. Experimentally, it was shown that by using the ratio NI/Np = 67%:33%, the performance or system performance for classifying the images of fingers leaves by using the LBP extraction method showed that training data was obtained the results close to 95% and testing data was obtained the results close to 76%, while by using the GLCM extraction showed that training data was obtained the results close to 83% and testing data was obtained the results close to 58%.