Analisis Regresi Spline Truncated pada Indeks Pembangunan Manusia (IPM) di Provinsi Jawa Timur tahun 2021

Ardiana Fatma Dewi, Kurnia Ahadiyah

Abstract


The increase in the achievement of the Human Development Index cannot be separated from the improvement of each of its constituent components. Currently, the components of the HDI also show an increase from year to year. To be able to participate in the development process, of course, Indonesian people are needed who are not only superior in terms of quantity, but also superior in terms of quality. HDI is used as a tool to achieve national goals, so that many things are related between humans and the development around them. This is to find out what factors can affect the HDI in East Java so that the provincial government can pay attention to several programs which can later be used to continue to maintain and improve development so that it can become an achievement for the Province of East Java. One of the analyzes that can be used is modeling, one of which is regression analysis. Nonparametric regression is a regression that is flexible in use because it can find its own data pattern. One of the truncated spline approaches to nonparametric regression can be used to predict the Human Development Index (HDI). HDI and several factors that influence it will be estimated at various knot points to get the best model. In the Spline Truncated nonparametric regression modeling which is applied to HDI data in East Java Province in 2021 several knot points are tried, namely 1 knot point, 2 knot point, and 3 knot point. The results obtained showed that the best model was found in the 3 knots experiment with a minimum GCV value of 5.40 and an R2 value of 89.875%.

Keywords


GCV; HDI; Knot Point; Spline Truncated

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DOI: http://dx.doi.org/10.12962/j27213862.v6i1.14107

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Inferensi by Department of Statistics ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/inferensi.

ISSN:  0216-308X

e-ISSN: 2721-3862

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