PENDEKATAN REGRESI SPLINE UNTUK MEMODELKAN POLA PERTUMBUHAN BERAT BADAN BALITA

  • NI LUH SUKERNI Udayana University
  • I KOMANG GDE SUKARSA Udayana University
  • NI LUH PUTU SUCIPTAWATI Udayana University

Abstract

The study is aimed to estimate the best spline regression model for toddler’s weight growth patterns. Spline is one of the nonparametric regression estimation method which has a high flexibility and is able to handle data that change in particular subintervals so thus resulting in model which fitted the data. This study uses data of toddler’s weight growth at Posyandu Mekar Sari, Desa Suwug, Kabupaten Buleleng. The best spline regression model is chosen based on the minimum Generalized Cross Validation (GCV) value. The study shows that the best spline regression model for the data is quadratic spline regression model with six optimal knot points. The minimum GCV value is 0,900683471925 with the determination coefficient  equals to 0,954609.

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Author Biographies

NI LUH SUKERNI, Udayana University

Program Studi Matematika, FMIPA – Universitas Udayana

I KOMANG GDE SUKARSA, Udayana University

Program Studi Matematika, FMIPA – Universitas Udayana

NI LUH PUTU SUCIPTAWATI, Udayana University

Program Studi Matematika, FMIPA – Universitas Udayana

Published
2018-09-02
How to Cite
SUKERNI, NI LUH; SUKARSA, I KOMANG GDE; SUCIPTAWATI, NI LUH PUTU. PENDEKATAN REGRESI SPLINE UNTUK MEMODELKAN POLA PERTUMBUHAN BERAT BADAN BALITA. E-Jurnal Matematika, [S.l.], v. 7, n. 3, p. 259-263, sep. 2018. ISSN 2303-1751. Available at: <https://ojs.unud.ac.id/index.php/mtk/article/view/41903>. Date accessed: 07 aug. 2020. doi: https://doi.org/10.24843/MTK.2018.v07.i03.p212.
Section
Articles

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