Bandingkan metode

	Analisis Sensitivitas Robust	Simulasi Monte Carlo
Bidang	Simulasi	Pengambilan Keputusan
Keluarga	Process / pipeline	MCDM
Tahun asal	1990s–2000s	1949
Pencetus	Saltelli, A. and colleagues	Metropolis, N., Ulam, S.
Tipe	Simulation-based robustness assessment pipeline	Robustness wrapper — Monte Carlo uncertainty propagation
Sumber perintis	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 9780470059975	Metropolis, N., Ulam, S. (1949). The Monte Carlo method. Journal of the American Statistical Association
Alias	RSA, Robust SA, Sensitivity Analysis under Uncertainty, Uncertainty-robust sensitivity analysis	—
Terkait	3	0
Ringkasan	Robust Sensitivity Analysis (RSA) systematically evaluates how much variation in model outputs can be attributed to uncertainty or variation in model inputs, with an explicit focus on conclusions that remain valid across a wide range of plausible input conditions. It goes beyond standard sensitivity analysis by asking not only which inputs matter most, but which findings are truly robust — stable regardless of assumptions made under uncertainty.	MONTE-CARLO-SIMULATION (Monte Carlo Simulation — Stochastic uncertainty propagation through MCDM model) is a ranking multi-criteria decision-making (MCDM) method introduced by Metropolis, N., Ulam, S. in 1949. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.