IMPLEMENTATION OF MISSING VALUES HANDLING METHOD FOR EVALUATING THE SYSTEM/COMPONENT MAINTENANCE HISTORICAL DATA - Hartini, Entin (Jurnal Teknologi Reaktor Nuklir Tri Dasa Mega, 2017)
Missing values are problems in data evaluation. Missing values analysis can resolve the problem of incomplete data that is not stored properly. The missing data can reduce the precision of calculation, since the amount of information is incomplete. The purpose of this study is to implement missing values handling method for systems/components maintenance historical data evaluation in RSG GAS. Statistical methods, such as listwise deletion and mean substitution, and machine learning (KNNI), were used to determine the missing data that correspond to the systems/components maintenance historical data. Mean substitution and KNNI methods were chosen since those methods do not require the formation of predictive models for each item which is experiencing missing data. Implementation of missing data analysis on systems/components maintenance data using KNNI method results in the smallest RMSE value. The result shows that KNNI method is the best method to handle missing value compared with listwise deletion or mean substitution.