Analysis of Unmet Need for Health Services Based on the Percentage of Public Health Complaints with a Kernel Estimator Approach - Rifada, Marisa; Amelia, Dita; Setyaningrum, Jeny Praesti; Septiandini, Niswah; Kalista, Yovita Karin; Dwitya, Shabrina Nareswari (JTAM (Jurnal Teori dan Aplikasi Matematika), 2025)
Healthcare services are a fundamental need that governments must guarantee to ensure optimal health outcomes for all citizens. However, many individuals still face significant barriers in accessing necessary healthcare services. This quantitative research employs a spatial analysis to examine the unmet need for health services based on public health complaints, utilizing a nonparametric regression approach with Kernel estimator. The Kernel estimator method was chosen for its flexibility in capturing unstructured data patterns, allowing the analysis to better reflect real-world conditions. The study uses health complaint data from the Central Bureau of Statistics, covering 38 provinces in Indonesia in 2024. However, data from 4 provinces were incomplete, so only 34 provinces were included in the analysis. The independent variable is the percentage of public health complaints, while the dependent variable is the percentage of unmet healthcare needs. A Gaussian kernel function was applied for nonparametric regression, identified as the optimal method based on the lowest Generalized Cross Validation (GCV) value of 1.052939 at a bandwidth of 0.33. The model demonstrates high predictive accuracy, with an R² of 82.44% and a Mean Squared Error (MSE) of 30.7%. These findings provide actionable insights for targeting healthcare disparities and improving service accessibility.