Classification of Civet and Canephora coffee using Support-Vector Machines (SVM) algorithm based on order-1 feature extraction

Abstract

Identification of coffee beans types is a process carried out in determining the category of civet and robusta coffee. The selection of civet coffee manually by the community in determining the variety of coffee beans. Manual selection makes the process slow. There is no certainty whether the selected coffee stated original civet coffee or a mixture with Robusta coffee. It needs to be built a system in identifying the types of coffee beans. In this study, the classification technology used for the selection process of Civet and Robusta coffee beans. This research uses first-order feature extraction with the Support-Vector Machines (SVM) algorithm, which aims to recognize civet and robusta coffee beans’ patterns using texture analysis on grayscale images and feature extraction. In the study, the authors used the Rapid Application Development approach to build the selection system. The data used in this study were 120 images, consisting of 110 training data and 10 test data. The accuracy in identifying the types of coffee beans by using this method is 87.27%.