Comparison of Min-Max normalization and Z-Score Normalization in the K-nearest neighbor (kNN) Algorithm to Test the Accuracy of Types of Breast Cancer

Henderi Henderi, Tri Wahyuningsih, Efana Rahwanto

Abstract


The purpose of this study was to examine the results of the prediction of breast cancer, which have been classified based on two types of breast cancer, malignant and benign. The method used in this research is the k-NN algorithm with normalization of min-max and Z-score, the programming language used is the R language. The conclusion is that the highest k accuracy value is k = 5 and k = 21 with an accuracy rate of 98% in the normalization method using the min-max method. Whereas for the Z-score method the highest accuracy is at k = 5 and k = 15 with an accuracy rate of 97%. Thus the min-max normalization method in this study is considered better than the normalization method using the Z-score. The novelty of this research lies in the comparison between the two min-max normalizations and the Z-score normalization in the k-NN algorithm.

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Keywords


K-nearest neighbors; Min-Max Normalization; Z-Score Normalization; Breast Cancer

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International Journal of Informatics and Information Systems (IJIIS)

2579-7069 (Online)
Organized by Universitas Amikom Purwokerto - Indonesia, Asosiasi Perguruan Tinggi Informatika dan Ilmu Komputer (APTIKOM) [MOU] and Bright Publisher
Published by Bright Publisher
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