Abstract
The surface response method is similar to the regression analysis method which uses procedures or ways of estimating the response function regression model based on the Ordinary Least Square (OLS) method. Unfortunately, using the quadratic method has no drawbacks because it is easily sensitive to assumption deviations due to outlier cases. One of the solutions to the outlier problem is using robust regression. The method of parameters in the regression is very diverse, but the methods used in this study are the Least Trimmed Square (LTS) and MM-estimator methods because both methods have a high breakdown point of nearly 50%. The variables studied were the response variable consisting of red roselle plant height (Y1) and red roselle flower weight (Y2). While the independent variables were soil moisture factor (X1) and NPK fertilizer application factor (X2). The purpose of this study is to estimate the response surface regression parameters. using the LTS and MM-estimator methods on data that contains outliers. The resulting model in data analysis shows the same result that the best model is using the LTS estimation method. The modeling result of plant height obtained an R-Square value of 98,27% with an error is 1,243. Meanwhile, for the red rosella plant flower weight model, the R-Square value was 97,31% with an error is 0.6632.
Highlights
The experiment is an activity that is often carried out in all fields of science and knowledge, especially science and technology
The response surface method is a combined method of mathematics and statistics used to model a response, in this case, it is usually the quality of a product that is influenced by certain variables to optimize the response (Shemi & Procter, 2018)
It can be said that this method makes use of the results of the experimental design and uses the help of statistics to find the optimal value of a response
Summary
The experiment is an activity that is often carried out in all fields of science and knowledge, especially science and technology. The OLS method is a method that is usually used to estimate parameter values in the second-order response surface method equation. Rousseeuw & Hubert, (2018) introduce a robust regression method that can be used to solve outlier cases in data and produce a strong model for outliers. Previous research on LTS was conducted by (Wulandari et al, 2013) and MM-estimator by (Yuliana et al, 2014) where their statements stated that each person has advantages and disadvantages. Both of these parameter estimation methods are used because they both have quite high breakdown points of almost 50%. The breakdown point value shows a measure of the robustness of an estimator parameter (Rousseeuw & Hubert, 2018)
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