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| Analisis Sensitivitas dengan Desain Komposit Pusat× | Metodologi Permukaan Respons (RSM)× | |
|---|---|---|
| Bidang | Desain Eksperimen | Desain Eksperimen |
| Keluarga≠ | Process / pipeline | Hypothesis test |
| Tahun asal≠ | 1951 (CCD); SA integration throughout 1970s–2000s | 1951 |
| Pencetus≠ | G. E. P. Box and K. B. Wilson (CCD); sensitivity analysis formalised within RSM by Montgomery and subsequent practitioners | George E. P. Box & K. B. Wilson |
| Tipe≠ | Quantitative experimental design with post-hoc sensitivity assessment | Second-order polynomial response surface model |
| Sumber perintis≠ | Box, G. E. P., & Wilson, K. B. (1951). On the Experimental Attainment of Optimum Conditions. Journal of the Royal Statistical Society: Series B, 13(1), 1–45. link ↗ | Box, G. E. P. & Wilson, K. B. (1951). On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B, 13(1), 1–45. link ↗ |
| Alias≠ | SA-CCD, CCD sensitivity analysis, RSM sensitivity analysis, response surface sensitivity study | RSM, Central Composite Design, Box-Behnken Design, CCD |
| Terkait≠ | 4 | 7 |
| Ringkasan≠ | Sensitivity analysis with Central Composite Design (CCD) combines a structured, space-filling experimental layout with a systematic examination of how much each input factor drives changes in the response. CCD supports estimation of a full quadratic response surface model; sensitivity analysis then interrogates that model to rank factors by influence, identify interactions, and map the performance landscape — guiding engineers and researchers toward robust operating conditions and efficient optimisation. | Response Surface Methodology is a collection of statistical and mathematical techniques for building an empirical second-order polynomial model that relates a continuous response variable to two or more controllable input factors, and then locating the factor settings that optimize that response. The approach was introduced by George E. P. Box and K. B. Wilson in their landmark 1951 paper and has since become a cornerstone of process optimization across engineering, chemistry, food science, and pharmaceutics. |
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