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Submitted
July 8, 2022
Accepted
September 1, 2022
Published
September 7, 2022
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Abstract

The deadliest infectious disease in Indonesia is tuberculosis (TB), and South Sulawesi is one of the provinces that contributed the most tuberculosis cases in Indonesia in 2018 with 84 cases per 100,000 population. This study aims to identify variables that could explain the proportion of TB cases in South Sulawesi. The data used has many explanatory variables, and there are outliers. Sparse Least Trimmed Squares (LTS) analysis can be used to handle data that has many explanatory variables and outliers. The resulting sparse LTS model successfully selects and shrinks the variables to 14 variables only. In addition, based on the value of R2 and RMSE for the model evaluation, the sparse LTS shows satisfying results rather than classical LASSO. The government can focus on these factors if they want to reduce the proportion of TB cases in South Sulawesi.

Keywords

LASSO Outliers Penalized regression Tuberculosis Robust regression

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How to Cite
Randa, T. M., Tinungki, G. M., & Sunusi, N. (2022). Modeling the Proportion of Tuberculosis Cases in South Sulawesi using Sparse Least Trimmed Squares. EKSAKTA: Journal of Sciences and Data Analysis, 3(2). https://doi.org/10.20885/EKSAKTA.vol3.iss2.art6
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