Open AccessPEMODELAN PRODUK DOMESTIK REGIONAL BRUTO (PDRB) DI PROVINSI JAWA TENGAH MENGGUNAKAN BOOTSTRAP AGGREGATING MULTIVARIATE ADAPTIVE REGRESSION SPLINES (BAGGING MARS)
Author(s) -
Maryam Jamilah An Hasibuan,
Agus Rusgiyono,
Diah Safitri
Publication year - 2019
Publication title -
jurnal gaussian : jurnal statistika undip
Language(s) - English
Resource type - Journals
ISSN - 2339-2541
DOI - 10.14710/j.gauss.v8i1.26628
Subject(s) - mars exploration program , multivariate adaptive regression splines , mathematics , statistics , multivariate statistics , econometrics , regression analysis , nonparametric regression , biology , astrobiology
Increased economic improvement is one way to improve people's welfare in certain areas. Gross Regional Domestic Product (GRDP) is one of the macroeconomic indicators used to measure economic growth in a region. Related to the economy in Central Java Province increased from year to year. Increasing economic growth is inseparable from the contribution of factors that sufficiently contribute to the GRDP. Factors that are the cause of GRDP are Regional Original Income, Foreign Investment, and Domestic Investment. The method used to model the factors that influence Gross Regional Domestic Product is the Multivariate Adaptive Regression Spline (MARS) method and combine it with Bagging. MARS method is one method that uses nonparametric regression and high dimension data. The best model used is a model with a combination of BF = 6, MI = 1, MO = 0 with GCV of 5.667,6680. Then bagging is done on the initial data set with 10, 25, 35, 40, 55, 75, 85, 90 and 100 bootstrap replications. GCV produced in bagging MARS 2.258,6192. GCV valuesobtained from MARS bagging are smaller compared to the MARS method. This shows that bagging can reduce the value of GCV and increase accuracy, making this method can be used in this study. Keywords: GRDP, GCV, MARS, Bagging