Perbandingan Kinerja Metode Regresi K-Nearest Neighbor dan Metode Regresi Linear Berganda pada Data Boston Housing

This research was made in order to see which method performance is better between the KNN (K-Nearest Neighbor) regression and multiple linear on Boston Housing data. The performace referred here MAE, RMSE, MAPE, R2. a predict something based closest training examples of an object. Meanwhile, forecasting technique involving more than one independent variable. comparison two methods results Mean Absolute Percent Error (MAPE). In this definitions distance used are Euclidean Minkowski distance. K value defines number nearest neighbors be examined determine dependent variable, we use values from 1 10 for each test data definition research, percentage 20%, 30%, 40% both methods. best MAPE obtained by 12,89% at = 3 13,22% Meanwhile 17,17%. as seen smaller method.