PERBANDINGAN MODEL KALIBRASI BERBASIS PLASMA-ACTIVATED WATER MENGGUNAKAN PRINCIPAL COMPONENT REGRESSION DAN PARTIAL LEAST SQUARE REGRESSION DALAM R

To ensure that the plasma reactor tool can simulate the Plasma Activated Water (PAW) liquid accurately. To ensure the quality of the PAW liquid according to the plasma reactor design, it is necessary to create a calibration model that can ensure that the model matches the observation data and improve the predictive ability of the model. The purpose of this article is to build a calibration model based on the quality of PAW liquid produced from a plasma reactor. This study used the Principal Component Regression (PCR) method and the Partial Least Square Regression (PLS-R) method. The advantage of the PCR method is that it reduces data based on correlation values. While the PLS-R method reduces data based on the most relevant factors in interpreting the data. Based on the experiments conducted, it was concluded that to build a calibration model based on plasma reactor data and PAW liquid data, the PCR method is better than the PLS-R method. This is shown based on the RMSEP and R2 values in the PCR method of 0.09625571 and 93.04699% while in the PLS-R method of 0.09873341 and 92.84436%. For the R2 value in the PCR method is greater which indicates that the data variant value is more acceptable to the calibration model than in the PLS-R method, then the RMSEP value in the PCR method is smaller which indicates that the statistical error value is more acceptable than PLS-R.