Evaluation of Factors Affecting Increased Unemployment in East Java Using NGWR-TS Method

Sifriyani Sifriyani, Budiantara I. N., Kartiko S. H., Gunardi Gunardi

Abstract


Objectives: In this study, an experimental investigation of how the factors that affect TPT in East Java Indonesia from a spatiotemporal perspective. Study Design: Data were taken from 38 Regencies/Cities in East Java obtained from the Central Bureau of Statistics (BPS). Variables used in this research consisted of two types, namely respond variables and eight predictor variables. The amount of data used in the research is as much as 382. Method: Nonparametric Truncated Spline in the Geographically Weighted Regression method (NGWR-TS) was performed, a total of 38 Area were treated as units of our analysis. Result: Based on the results of the study, it shows that the unemployment rate has a geographical influence spatial heterogeneity. The unemployment rate has an unknown regression curve so that the NGWR-TS method is feasible to be used for modeling of the Unemployment rate. The NGWR-TS method has a model goodness of 80.42%. Significant factors that influence the unemployment rate are the Percentage of the poor population, Percentage of Low-Educated or elementary school dropouts workforce, economic growth rate, Investment ratio workforce number, Regional minimum wage, Ratio of the amount of Large-Medium Industry workforce number, Percentage of people working in the agricultural sector and Area of agricultural land.

Conclusion: Each area in 38 Regencies/Cities has different significant variables so that each area has a different model from other areas. This is important for the Regional Government to take measures to reduce unemployment rates in East Java Province.


Keywords


Multivariate; Nonparametric geographically weighted Regression; Spatial; Truncated Spline; Unemployment Rate.

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References


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Sifriyani, Haryatmi, I.N Budiantara, and Gunardi., “Geographically Weighted Regression with Spline Approach”. Far East Journal of Mathematical Sciences, 101(6), 1183-1196, 2017.

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Sifriyani, I.N. Budiantara, S.H. Kartiko and Gunardi, “A New Method of Hypothesis Test for Truncated Spline Nonparametric Regression Influenced by Spatial Heterogeneity and Application”, Abstract and Applied Analysis. 2018, https://doi.org/10.1155/2018/9769150.

Sifriyani, “Multivariable Nonparametric Regression Truncated Spline in The Geographically Weighted Regression Models”, Ph.D, Universitas Gadjah Mada, Dept of Mathematics, Indonesia, 2018.


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