Pemodelan Peluang Pencemaran Air Sungai Menggunakan Model Geographically Weighted Logistic Regression (Studi Kasus: Data DO Air Sungai di Kalimantan Timur) - Adelia Miranda; Suyitno Suyitno; Meirinda Fauziyah (Jurnal Matematika, Statistika dan Komputasi, 2025)
Geographically Weighted Logistic Regression (GWLR) is a local binary logistic regression model, and it’s applied to the spatial heterogeneity data. The parameter estimation of GWLR model in this study uses Maximum Likelihood Estimation (MLE) method, and it’s conducted at each observation location with spatial weighting. The spatial weight in this study was calculated using the adaptive tricube function. The spatial weighting function depends on distance between observation location and bandwidth, where the determination of optimal bandwidth uses the Akaike Information Criterion (AIC). The aim of this research is to identify the factors influencing the probability of river water pollution in East Kalimantan Province through GWLR modelling to Dissolved Oxygen (DO) data 2022, and to interpret it based on the best model. The research data is secondary data provided by Life Environment Department of East Kalimantan Province. Research concludes that the GWLR was fit model based on the results of similarity testing of the GWLR model and global model, as well as simultaneous parameter testing, with the model fitting measure was a McFadden R-Squared value of 61,1%, and an AIC value of 29,629. Based on partial parameter testing, local factors influencing chance of river water pollution in East Kalimantan can be identified, namely nitrate concentration and water color degree. Based on the GWLR modelling to DO data 2022, it can be interpreted that increasing nitrate concentration and water colour degree respectively will increase the probability of river water pollution