Pemodelan Regresi Robust dengan Estimasi Generalized-M untuk Penanganan Outlier pada Kasus Kusta di Indonesia - Loleh, Linda Purnama Sari; Wungguli, Djihad; Rezky Friesta Payu, Muhammad; Nashar, La Ode (Pendidikan Matematika FKIP Universitas Mataram, 2025)
which attacks the skin, peripheral nerves, and other body organs except the central nervous system. New leprosy cases with visible disabilities are classified as Grade 2 disabled leprosy. The number of Grade 2 disabled leprosy cases is an indicator used to show success in early detection of new leprosy cases. However, the leprosy data in Indonesia for 2023 contains outliers that can affect the results of linear regression analysis. To address this issue, this study utilizes the robust regression method of generalized-M estimation, which is an extension of M-estimation. The objectives of this study are to obtain a robust regression model using generalized-M estimation and to identify significantly influential variables. The research findings indicate that these factors have a significant simultaneous impact on the number of leprosy cases with grade 2 disability, and partially, the factor of access to basic sanitation facilities has an influence on the number of leprosy cases with grade 2 disability with an R^2 value of 73%, which can be explained by the predictor variables in this study. Meanwhile, 27% is explained by other predictor variables not included in this study. From these results, it is hoped that the efforts of the government and relevant agencies can improve access to basic sanitation facilities for the prevention and control of leprosy cases in Indonesia.