Tuberculosis (TB) is an infectious disease, caused by mycobacterium tuberculosis that affects various organs, especially the lungs. If TB treatment is not done thoroughly by the patient, then it can lead to death. Tuberculosis disease could not be healed cause TB bacteria to double immunity against anti-TB drugs, called multi-drug resistant (MDR). One identification issue toward TB infection chain is a TB case distribution analysis using mathematical modeling. The method used in this study was Multivariate Adaptive Regression Spline (MARS). Multivariate Adaptive Regression Spline is one type of non-parametric regression techniques, where the model does not assume the functional relationship between response and predictor variables, and has a flexible functional structure as well. Modeling aims to determine the factors that have the most significant influence on MDR-TB cases in Lamongan regency, as well as predict the incidence of MDR-TB in each sub-regency. The results show, the best model has a combination of BF = 28, MI = 2 and MO = 3, based on the minimum GCV, which is 5.26E-06. Furthermore, the model is statistically proper according to the criteria of APER and Press's Q.

Original languageEnglish
Article number012017
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 15 Feb 2021
Event3rd International Conference on Statistics, Mathematics, Teaching, and Research 2019, ICSMTR 2019 - Makassar, Indonesia
Duration: 9 Oct 201910 Oct 2019


  • Multivariate
  • Regression Spline
  • Tuberculosis


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