Perbandingan Metode Hot-deck, Regression dan K-Nearest Neighbor Imputation dalam Pendugaan Data Hilang pada Dapodik Tahun 2020

Abstract

Data Pokok Pendidikan (Dapodik) is a nation-wide data collection system that contains data on education units. Missing value in Dapodik cause the loss of important information. To solve this problem can use imputation. Imputation is a procedure to predict the missing value with a certain method. This study aims to compare three imputation methods which are Hot-deck imputation, Regression Imputation and K-Nearest Neighbor imputation (KNNI). Simulation for generating missing value was carried out by dividing the percentage of  2%, 3%, 4% and 5%, then imputed with the three methods. The best model is determined based on the lowest value of RMSE and MAPE. The best imputation method based on the lowest RMSE and MAPE values is a regression imputation