KLASIFIKASI LEVEL KEMATANGAN BUAH TOMAT BERDASARKAN FITUR WARNA MENGGUNAKAN MULTI-SVM
DOI:
https://doi.org/10.35316/jimi.v1i1.442Keywords:
classification, multi-svm, knn, tomato, adaptive histogram equalizationAbstract
Grouping of tomato maturity level is one way to pay attention to the quality of the tomatoes. The traditional way takes a long time and low accuracy, since the determination of the level of subjectively assessed. In addition, the importance of the classification of the level of maturity of tomatoes due to a period of tomato maturation process is relatively quick, so it can reduce the risk of rotting tomatoes. The dataset used in this study was 108 tomato image taken using three types of smartphones. The dataset is divided into 66 training data and testing the data 42. Improvements to the image preprocessing stage is done with adaptive histogram equalization and compared with the histogram equalization. In the feature extraction using color features of the R, G, and A *. The classification of the level of maturity of tomato is done by comparing the accuracy of using multi-SVM and KNN. In the Multi-SVM method using the highest percentage of kernel functions RBG is equal to 77.84%. While the method kNN highest percentage was 77.79% using a value of k = 3.
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