(Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)(2) Norlida Hassan
(Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)(3) Shihab Hamad Khaleefah
(Faculty of Computer Science, Al Maarif University College, Iraq)(4) * Salama A Mostafa
(Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)(5) Azizul Azhar Ramli
(Faculty of Computer Science & Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia)*corresponding author
AbstractThe texture is the object’s appearance with different surfaces and sizes. It is mainly helpful for different applications, including object recognition, fingerprinting, and surface analysis. The goal of this research is to investigate the best classification models among the Naive Bayes (NB), Random Forest (DF), and k-Nearest Neighbor (k-NN) algorithms in performing texture classification. The algorithms classify the leaves and urban land cover of texture using several evaluation criteria. This research project aims to prove that the accuracy can be used on data of texture that have turned in a group of different types of data target based on the texture’s characteristic and find out which classification algorithm has better performance when analyzing texture patterns. The test results show that the NB algorithm has the best overall accuracy of 78.67% for the leaves dataset and 93.60% overall accuracy for the urban land cover dataset.
KeywordsData mining; Texture analysis; Random forest; Naive bayes; k-Nearest neighbor
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DOIhttps://doi.org/10.26555/ijain.v7i3.706 |
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