PENERAPAN ALGORITMA GLOWWORM SWARM OPTIMIZATION PADA MODEL GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN KERNEL ADAPTIF

  • I GEDE HARDI KARMANA Udayana University
  • LUH PUTU IDA HARINI Udayana University http://orcid.org/0000-0003-4439-2643
  • KETUT JAYANEGARA Udayana University
  • I PUTU EKA NILA KENCANA Udayana University

Abstract

This study aimed to apply glowworm swarm optimization (GSO) algorithm as an alternate way to obtain optimal bandwidth in geographically weighted regression (GWR) model with adaptive kernel function. The result showed that GSO was able to obtain optimal bandwidth with lower cross validation (CV) value than the traditional way that was using k-nearest neighbor (KNN) algorithm. Unfortunately, the running time of GSO was far slower than KNN was.

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Author Biographies

I GEDE HARDI KARMANA, Udayana University

Program Studi Matematika, Fakultas MIPA – Universitas Udayana

LUH PUTU IDA HARINI, Udayana University

Program Studi Matematika, Fakultas MIPA – Universitas Udayana

KETUT JAYANEGARA, Udayana University

Program Studi Matematika, Fakultas MIPA – Universitas Udayana

I PUTU EKA NILA KENCANA, Udayana University

Program Studi Matematika, Fakultas MIPA – Universitas Udayana

Published
2020-01-31
How to Cite
KARMANA, I GEDE HARDI et al. PENERAPAN ALGORITMA GLOWWORM SWARM OPTIMIZATION PADA MODEL GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN KERNEL ADAPTIF. E-Jurnal Matematika, [S.l.], v. 9, n. 1, p. 79-84, jan. 2020. ISSN 2303-1751. Available at: <https://ojs.unud.ac.id/index.php/mtk/article/view/57294>. Date accessed: 04 nov. 2024. doi: https://doi.org/10.24843/MTK.2020.v09.i01.p282.
Section
Articles

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