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Generalized Space Time Autoregressive (GSTAR)(p;λk) model is one of space time model that used for modeling time series with location relevance (p) is the autoregressive order and λk for the first order of spatial. Location relevance can be represented with a weighted. The number of patient TB Paru (BTA+) in DKI Jakarta is one of time series data with location relevance so can be used for GSTAR model. In this research, the number of patient TB Paru (BTA+) in DKI Jakarta were modeled with GSTAR model using inverse distance weighted and normalization of cross correlation weighted. Furthermore, a better model GSTAR is chosen by the smallest RMSE. The result of this research for the number of patient TB Paru (BTA+) is GSTAR (1;1) model. GSTAR (1;1) model by using normalization of cross correlation weighting is better than that model using inverse distance weighting. Because GSTAR (1;1) model by using normalization of cross correlation weighting has root mean square error (RMSE) 73,57728 less than GSTAR (1;1) model by using inverse distance weighting.

Pemodelan Generalized Space Time Autoregressive (GSTAR) dan Penerapannya pada Penderita TB Paru (BTA+) di DKI Jakarta