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

IJIIS : International Journal of Informatics and Information Systems

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Title 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
 
Creator Henderi, Henderi
Wahyuningsih, Tri
Rahwanto, Efana
 
Subject K-nearest neighbors; Min-Max Normalization; Z-Score Normalization; Breast Cancer
 
Description 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.
 
Publisher Bright Publisher
 
Contributor
 
Date 2021-03-01
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://ijiis.org/index.php/IJIIS/article/view/73
10.47738/ijiis.v4i1.73
 
Source International Journal of Informatics and Information Systems; Vol 4, No 1: March 2021; 13-20
2579-7069
10.47738/ijiis.v4i1
 
Language eng
 
Relation http://ijiis.org/index.php/IJIIS/article/view/73/32
 
Rights Copyright (c) 2021 IJIIS: International Journal of Informatics and Information Systems
https://creativecommons.org/licenses/by-sa/4.0
 

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