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

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

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

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.

Henderi 1 , Tri Wahyuningsih 2,* , Efana Rahwanto

2021

PERI IRAWAN

PDF

ENGLIST

TEXT