Abstract Developments and advancements in the field of Technology and Information have a considerable influence in the world of image analysis. At present, the process of image manipulation is easier to do, one of the factors in the emergence of various methods in image segmentation. Image segmentation is the first step in doing image processing, pattern recognition, computer vision, because most image processing processes depend on the results of the enhancement operation or image repair process. This final project will be implemented in the process of determining the type of oil palm plantation land using the Naïve Bayes method. The repair process starts from the RGB image to Greyscale, then proceed to the histogram equalization process, then proceed with the inverse image process. The feature extraction process is carried out after image repair operations using the co-occurrence matrix method. The extraction process of the co-occurrence matrix features 6 features, namely angular second moment value, contrast, correlation, varience, inverse different moment, and entropy. The Naïve Bayes process is one process for classifying a class data. There are four classes used in this system test, namely Young Palm Oil, Mature Palm Oil, and Old Palm Oil. Class determination is based on the largest value as the appropriate class. Based on the above objectives, a system can be created using the Matlab R2011b application program. The computation is done by using image images of various types of oil palm trees on plantations in Kalimantan which are taken from aerial photographs which are then cropped to be sampled with a pixel size of 60X60 in 400 images.
Pengelompokan Buah Jeruk menggunakan Naïve Bayes dan Gray Level Co-occurrence Matrix
