PEMODELAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR) PADA KASUS MALARIA DI INDONESIA - Moh Yamin Darsyah (Jurnal Litbang Edusaintech, 2021)
Geographically Weighted Negative Binomial Regression Modeling by Comparing Adaptive Gaussian Weighting and Adaptive Tricube in Cases of Malaria in Indonesia. Malaria was an infectious disease caused by the bite of female malaria mosquitoes (Anopheles) caused by the Plasmodium parasite that breeds in human red blood cells. Malaria is one of the biggest causes of death in Indonesia, so it needs special handling in preventing the number of Indonesian malaria cases. The spread of malaria cases was caused by population density, the households with clean and healthy living behaviors (PHBS), the sufferers receiving ACT programs, and the households living in slums. Indonesia is a unitary state that has a large area and certainly has different environmental characteristics. So that spatial regression analysis is the right solution for the case of Malaria in Indonesia. The spatial regression analysis used is Geographically Weighted Negative Binomial Regression (GWNBR) is one of the models on spatial points. The purpose of this study is to determine the best modeling using GWNBR with malaria cases in Indonesia and the factors that influence it from a regional perspective by comparing the Adaptive Gaussian weighting matrix and Adaptive Tricube weighting matrix. The results showed that the best modeling with the smallest AIC value of 695,2341962 was Geographically Weighted Negative Binomial Regression (GWNBR) with Adaptive Tricube weighting. Significant variable are population density, provision of ACT treatment and slums by taking samples from Papua Province as the province with the highest number of malaria cases.