Model Geographically Weighted Univariat Weibull Regression pada Data Indikator Pencemaran Air Dissolve Oxygen di Daerah Aliran Sungai Mahakam Kalimantan Timur Tahun 2018 - Sugiarto, Sugiarto; Suyitno, Suyitno; Rizki, Nanda Arista (EKSPONENSIAL, 2021)
Geographically Weighted Univariat Weibull Regression (GWUWR) model is a regression model applied to spatial data. Parameter estimation of GWUWR model is performed at every observation location using spatial weighting. The purpose of this study is to determine the GWUWR model at the water pollution indicator data namely dissolved oxygen (DO) at Mahakam river in East Kalimantan and to find out the factors that influence DO in Mahakam river. The research data are secondary from the environmental services East Borneo. The research response variable was DO, meanwhile the predictor variables were pH, Total Dissolve Solid, Total Suspended Solid, Nitrate and Amonia. Parameter estimation method is Maximum Likelihood Estimation (MLE). Spatial weighting was determined using the Adaptive Gaussian weighting function and optimum bandwidth determination criteria used Generalized Cross-Validation (GCV). Based on the result of the parameter testing of GWUWR model it was concluded the factors influencing DO locally were pH, Total Dissolve Solid and ammonia concentrations, while the factors influencing globally were Total Dissolve Solid and ammonia concentration