EVALUATION OF THE K-NEAREST NEIGHBOR MODEL WITH K-FOLD CROSS VALIDATION ON IMAGE CLASSIFICATION

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M. Rhifky Wayahdi
Dinur Syahputra
Subhan Hafiz Nanda Ginting

Abstract

In this paper, the data used is the banana image which is extracted into the dataset into 4 attributes, namely red, green, blue, and the mean for the classification process. Image data is classified using the k-Nearest Neighbor method which will be optimized the model with the k-Fold Cross Validation algorithm. Evaluation of the k-NN model with the k-FCV algorithm can improve accuracy and can build better machine learning models in the image classification process. The default K-NN obtained an accuracy rate of 57%, while the results of the model evaluation with the k-FCV algorithm, on fold 3 obtained an accuracy rate of 68%. The percentage yield with the new model increased by 11% which indicates that the machine learning model that was built was quite optimal

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How to Cite
Wayahdi, M. R., Syahputra , D., & Hafiz Nanda Ginting , S. (2020). EVALUATION OF THE K-NEAREST NEIGHBOR MODEL WITH K-FOLD CROSS VALIDATION ON IMAGE CLASSIFICATION. INFOKUM, 9(1,Desember), 1-6. Retrieved from http://infor.seaninstitute.org/index.php/infokum/article/view/72

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