MODELING OF POVERTY INDICATORS IN EAST JAVA PROVINCE USING BOOTSTRAP AGGREGATING MULTIVARIATE ADAPTIVE REGRESSION SPLINE (BAGGING MARS)

danu priambodo(1*), Rochdi Wasono(2), M. Al Haris(3)


(1) Department of Statistics, Universitas Muhammadiyah Semarang, Semarang, Indonesia
(2) Department of Statistics, Universitas Muhammadiyah Semarang, Semarang, Indonesia
(3) Department of Statistics, Universitas Muhammadiyah Semarang, Semarang, Indonesia
(*) Corresponding Author

Abstract


Poverty is a situation where a person is below the minimum standard value line. The view regarding poverty can be said that poverty is a multidimensional phenomenon where there are many indicators that influence poverty, so modeling needs to be carried out to find out what indicators influence poverty. This research uses The Multivariate Adaptive Regression Spline (MARS) with Bootstrap Aggregating. MARS is a nonparametric regression method that can handle high- dimensional data. The best model produced by MARS is a combination of BF=24, MI=1, MO=0 with a GCV of 9.231184. Then Bagging was carried out on the initial dataset with 35, 45, 50, 75 and 100 bootstrap replications. The best model was produced by MARS Bagging on 45 replications with a GCV of 3.84492. The GCV value obtained by Bagging MARS is smaller than MARS. This shows that Bagging can reduce GCV and increase accuracy, so this method can be used in this research.

Keywords


Bagging, GCV, Poverty, MARS

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