PEMODELAN REGRESI QUANTIL DENGAN KERNEL SMOOTHING PADA FAKTOR-FAKTOR YANG MEMPENGARUHI PENYEBARAN API MALARIA DI INDONESIA: (Quantile Regression Modeling with Kernel Smoothing on Factors Affecting the Spread of Malaria Fire in Indonesia)

Muhammad Yahya Matdoan (Universitas Pattimura)
Mozart Wiston Talakua (Universitas Pattimura)
Ronald John Djami (Universitas Pattimura)

Uniqbu Journal of Exact Sciences
Vol. 1 No. 2 (2020): Uniqbu Journal of Exact Sciences (UJES)

Regression analysis method is one of the statistical methods used to describe the relationship between two or more variables, so that a variable can be predicted from another variable. In regression analysis there are two types of approaches, namely parametric and nonparametric approaches. Estimates used to estimate the parameters in the regression analysis using the OLS method. This method is based on the mean distribution, so it is not appropriate to analyze a number of data that are not symmetrical or contain outliers. Therefore, a quantile regression method and kernel smoothing were developed that were not affected by data containing outliers and could also be used as an alternative to solving fluctuating data problems. This study uses quantile regression with kernel smoothing in the case of factors affecting malaria in Indonesia. The results show that the main factors causing the spread of malaria in Indonesia are access to proper sanitation, household factors that behave in a clean and healthy life, and the number of puskesmas and the percentage of medical personnel.