BURR XII REGRESSION AND OPTIMIZATION FOR DIARRHEA INCIDENCE ANALYSIS IN SURAKARTA CITY - Rizwan Arisandi; Adhe Lingga Dewi; Mohammad Fajri (Jurnal Gaussian, 2026)
Diarrhea remains a major public health concern in Indonesia, with Surakarta recording 11,434 cases in 2024, an increase of 58% from the previous year. This study applies the Burr XII regression model to identify environmental factors influencing diarrhea incidence and accommodate heavy-tailed variability across urban villages. The Burr XII model was chosen for its robustness in handling skewed data and extreme incidence patterns commonly found in epidemiological studies. Parameter estimation was carried out through the Maximum Likelihood Estimation (MLE) approach, with the BFGS algorithm as the primary optimization method, compared against Genetic Algorithm (GA) and Simulated Annealing (SANN). This research is important due to the increasing trend of diarrhea cases and the need for accurate statistical models to support public health policies. The results indicate that the BFGS method achieved the best fit, with five significant predictors: distance to the nearest hospital, rainfall, distance to waste disposal site, elevation, and distance to the nearest river. Population density and slope were not statistically significant but retained for theoretical relevance. These findings emphasize the importance of environmental and infrastructural factors in diarrhea prevention and support spatially targeted public health interventions.