PENGGUNAAN METODE GLCM PADA APLIKASI PENGOLAHAN CITRA PENENTUAN KUALITAS HASIL BIJI KAKAO DI SULAWESI TENGAH

Yusuf Anshori, Dessy Santi, Yayang Febrina, Dwi Shinta Angreni, Hardiyanti Sultan, Muhammad Fadil

Abstract

Cocoa is the leading commodity of plantations in Central Sulawesi, Indonesia. Cocoa is a commercial crop among the annual crops grown by most smallholders. Although the demand for this commodity is always high, its development has been hampered due to the lack of understanding of small farmers on the importance of quality cocoa beans in the market share. Current technological advances can be the main solution to overcome the problem of the weak competitiveness of cocoa beans in Central Sulawesi. There have been many uses of information technology, namely image processing methods used to classify the quality of cocoa beans. Therefore, a study is proposed using the GLCM texture feature extraction method and comparing the accuracy of the SVM, KNN, and NN classification methods in image processing applications for determining the quality of cocoa beans in Central Sulawesi. The results showed that using the GLCM texture feature method for feature extraction on cocoa bean images can achieve good classification accuracy. The three classification methods used produce accuracy above 90%.

Keywords

Image Processing; Machine Learning; Computer Vision; Cacao Beans;

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