Analisis Performa Normalisasi Data untuk Klasifikasi K-Nearest Neighbor pada Dataset Penyakit

Abstract

This study investigates four normalization methods (Min-Max, Z-Score, Decimal Scaling, MaxAbs) across prostate, kidney, and heart disease datasets for K-Nearest Neighbor (K-NN) classification. Imbalanced feature scales can hinder K-NN performance, making normalization crucial. Results show that Decimal Scaling achieves 90.00% accuracy in prostate cancer, while Min-Max and Z-Score yield 97.50% in kidney disease. MaxAbs performs well with 96.25% accuracy in kidney disease. In heart disease, Min-Max and MaxAbs attain accuracies of 82.93% and 81.95%, respectively. These findings suggest Decimal Scaling suits datasets with few instances, limited features, and normal distribution. Min-Max and MaxAbs are better for datasets with numerous instances and non-normal distribution. Z-Score fits datasets with a wide range of feature numbers and near-normal distribution. This study aids in selecting the appropriate normalization method based on dataset characteristics to enhance K-NN classification accuracy in disease diagnosis. The experiments involve datasets with different attributes, continuous and categorical data, and binary classification. Data conditions such as the number of instances, the number of features, and data distribution affect the performance of normalization and classification.