PENERAPAN SPATIAL DURBIN MODEL PADA DATA PENYAKIT MALARIA DI INDONESIA - Nabilla, Maghrisa Ayu; Hayati, Memi Nor; Sifriyani, Sifriyani (Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika, 2025)
The Spatial Durbin Model (SDM) is a special case of the Spatial Autoregressive (SAR) model, involving the addition of spatial lag effects of both the dependent and independent variables. The parameter estimation used in this study is the maximum likelihood estimator. Parameter estimation for the SDM is performed at each observation location using spatial weighting. The spatial weights are calculated based on queen contiguity and customized contiguity weighting methods. This study aims to obtain the SDM and identify the factors influencing the number of malaria cases in Indonesia in 2023. The Lagrange Multiplier (LM) test indicates that there is a spatial lag in the dependent variable, with the parameter ρ being significant at a significance level of α = 0.1. Based on the results of the SDM analysis, it was found that the factors directly influencing the number of malaria cases in Indonesia in 2023 are the percentage of poor population, number of medical personnel and the percentage of households with access to adequate drinking water services. Meanwhile, the factors that have an indirect or spatial lag effect are the open unemployment rate and the percentage of poor population.