Perbandingan Metode KNN, Naive Bayes, dan Regresi Logistik Binomial dalam Pengklasifikasian Status Ekonomi Negara

N. K. Kutha Ardana (IPB University)
Ruhiyat Ruhiyat (IPB University)
Nurfatimah Amany (IPB University)
Teofilus Kevin Irawan (IPB University)
Raymond Raymond (IPB University)
Rizalius Karunia (IPB University)
Syifa Fauzia (IPB University)

Jambura Journal of Mathematics
Vol 5, No 2: August 2023

The classification of a country's economic status as developed or developing often involves factors such as life expectancy and its underlying variables. This research aims to compare the performance of three machine learning algorithms, namely KNN (K-Nearest Neighbors), naive Bayes, and binomial logistic regression, in classifying the economic status of countries as developed or developing. The data used in this study is "Life Expectancy (WHO) Fixed," obtained from the Kaggle website. The first statistical analysis conducted was Principal Component Analysis (PCA) using 16 predictor variables. PCA resulted in three principal components capable of explaining 71.41% of the variance, which were subsequently used in the KNN, naive Bayes, and binomial logistic regression methods. The analysis results from the KNN, naive Bayes, and binomial logistic regression methods produced F1-scores of 100\%, 98.19%, and 97.36%, respectively.