Open AccessMODELING THE LEVEL OF OPEN UNEMPLOYMENT IN CENTRAL JAVA WITH MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS) APPROACH
Author(s) -
Erma Oktania Permatasari,
Firda Nasuha,
Carlos Lazaro Prawirosastro
Publication year - 2021
Publication title -
jurnal asro (analisis sistem dan riset operasi)/jurnal asro
Language(s) - English
Resource type - Journals
eISSN - 2722-5763
pISSN - 2460-7037
DOI - 10.37875/asro.v12i01.382
Subject(s) - multivariate adaptive regression splines , java , unemployment , population , econometrics , regression analysis , nonparametric regression , statistics , multivariate statistics , economics , nonparametric statistics , computer science , mathematics , demography , macroeconomics , sociology , programming language
The level open unemployment is a value that shows the number of working-age population who looking for work, is preparing a business, feels impossible to get a job or already have a job but have not started working and often used for measured employment. Like at Central Java has increasing the total population at 2014 and have high total investation whereas should be can getting more employment, but actually still give high unemployment about 996.344 population at 2014. So that, in this research used nonparametric regression approach which multivariate adaptive regression splines (MARS) for modeling the level open unemployment in Central Java at 2014 because the level open unemployment in Central Java predicted influence by some factors. This research resulted in the best modeling for level of open unemployment in Central Java Province with value of GCV minimum that obtained at 0,396 with R-square at 86,5 percent as well as the predictor variables were entered into the model as much as three, namely the total population with interest rate of 100 percent, the minimum wage with interest rate of 41,955 percent, and the total working population with interest rate of 39,547 percent.
Keywords: MARS, Nonparametric regression, Level of open unemployment