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

Ardiana Fatma Dewi, Kurnia Ahadiyah


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%.


GCV; HDI; Knot Point; Spline Truncated

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Badan Pusat Statistik (BPS). Statistik Indonesia 2020.

Badan Pusat Statistik (BPS). Statistik Indonesia dalam Infografis 2021.

R. N. S. Setiawan, I.N. Budiantara, and V. Ratnasari, Application of Confidence Intervals for Parameters of Nonparametric Spline Truncated Regression on Index Development Gender in East Java, IPTEK Journal of Science, vol. 2, no.3, pp.49–55, 2017.

I. Y. A. Polanagau, Sifriyani, & Wasono, Pemodelan Regresi Nonparametrik Spline Truncated dan Aplikasinya pada Indeks Pembangunan Manusia di Pulau Kalimantan. Prosiding Seminar Nasional Matematika dan Statistika. 130-137. 2019.

F. Kusunartutik & N. K. Dwidayati, Pemilihan Titik Knot Optimal Menggunakan Metode GCV dalam Regresi Nonparametrik Spline Truncated, Indonesian Journal of Mathematics and Natural Sciences, vol.45, no.2, pp. 69-76, 2022.

R. Hidayat, Yuliani & M. Sam, Model Regresi Nonparametrik dengan Pendekatan Spline Truncated, Prosiding Seminar Nasional, vol.03, no.1, pp. 203-210, 2018.

J. R. L. Eubank, Nonparametric Regression and Spline Smoothing. New York: Marcel Dekker, 1999

I. N. Budiantara, Regresi Nonparametrik Spline Truncated. Surabaya: ITS Press, 2019.

R. Fidella, The Factors Affecting HDI Indonesia, International Journal of Science, Engineering, and Technology, vol. 8, no.6, pp. 160-167, 2021.

A. F. Dewi, I. N. Budiantara & V. Ratnasari, Parameter Estimation of Spline Truncated, Kernel, and Fourier Series Mixed Estimators in Semiparametric Regression, AIP Conference Proceedings vol. 2540, Issue 1, 2023.

W. Hardle, Applied Nonparametric Regression. New York: Cambridge University Press, 1990.

G. Wahba, Spline Models for Observational Data. Pennsylvania: SIAM, 1990

R. L. Eubank, & W. Thomas, Detecting Heterocedasticity in Nonparametric Regression. Journal of the American Statistical Association, 387-392. 1993.

I. N. Budiantara, M. Ratna, I. Zain, dan W. Wibowo, “Modeling the Percentage of Poor People in Indonesia Using Spline Nonparametric Regression Approach,”International Journal of Basic & Applied Sciences IJBAS-IJENS, vol. 12, no. 06, pp. 119– 124, 2012.

A. T. R. Dani, V. Ratnasari, and I. N. Budiantara, Optimal Knots Point and Bandwidth Selection in Modeling Mixed Estimator Nonparametric Regression, IOP Conference Series: Materials Science and Engineering,vol.1115, 012020, 2021.



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e-ISSN: 2721-3862

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