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A new procedure of space-time modeling through the Invers of Autocovariance Matrix (IAcM) is proposed. By evaluating the IAcM behaviors on behalf of the Generalized Space-Time Autoregressive (GSTAR) process stationarity, we may find an appropriate model to space-time data series. This method can complete the Space-Time ACF and PACF methods for identifying space-time models. For study case, we apply the GSTAR models to the monthly tea production of some plantations in West Java, Indonesia.