Analisis Regresi Logistik Multinomial pada Kasus Kepemilikan Jaminan Kesehatan Usia Produktif - Arista Marlince Tamonob; Anugrah Jordan; Robertus Dole Guntur; Farly Oktriany Haning (Griya Journal of Mathematics Education and Application, 2026)
Health insurance ownership among the productive-age population is a critical issue in health development, particularly in Southwest Sumba Regency, which has not yet achieved the Universal Health Coverage (UHC) target. This study aims to identify the factors influencing health insurance ownership using the Multinomial Logistic Regression (MLR) method and to evaluate the model’s performance in classifying health insurance ownership status. The data used are derived from the 2023 National Socio-Economic Survey (SUSENAS) published by Statistics Indonesia of East Nusa Tenggara Province. The results of the MLR analysis indicate that place of residence, age, marital status, ownership of a savings account, type of employment, level of education, and health status have a significant effect on health insurance ownership. In addition, the model’s classification accuracy is evaluated using the Apparent Error Rate (APER) and overall accuracy, showing that the MLR model has a reasonably good classification ability in predicting health insurance ownership categories. These findings provide an empirical basis for policy formulation aimed at increasing health insurance coverage in Southwest Sumba Regency.