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Tasyah, Qhiky Lioni
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Computer Science & IT Industrial & Manufacturing Engineering Mathematics

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Pemodelan IPM di Provinsi Bengkulu dengan Pendekatan Metode Geographically Weighted Regression (GWR) dan Geographically Temporally Weighted Regression (GTWR) Oktarina, Cinta Rizki; Rizal, Jose; Faisal, Fachri; Tasyah, Qhiky Lioni; Pratiwi, Stevy Cahya
Jurnal EurekaMatika Vol 12, No 1 (2024): Jurnal EurekaMatika
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jem.v12i1.66629

Abstract

The Geographically Temporally Weighted Regression (GTWR) method is a development of the Geographically Weighted Regression (GWR) method, namely by considering elements of location and time. This research aims to obtain the best estimation results between the GWR and GTWR methods applied to human development index data in Bengkulu Province for 2018–2022. There are three variables modelled, namely three independent variables: life expectancy, average years of schooling, and open unemployment rate, while the dependent variable is the Human Development Index. The research results show that the three independent variables significantly influence the dependent variable and have spatial heterogeneity in the modelled data. In addition, the coefficient of determination value for GTWR is 99.98%, while for GWR it is 99.74%, so the GTWR method is better for modelling the Human Development Index in Bengkulu Province for 2018–2022.Keywords: Coefficient of Determination, GWR Method, GTWR Method, Human Development Index, Spatial heterogeneity.AbstrakMetode Geographically Temporally Weighted Regression (GTWR) merupakan pengembangan dari metode Geographically Temporally Weighted Regression (GWR), yakni dengan mempertimbangkan unsur lokasi dan waktu. Penelitian ini bertujuan untuk mendapatkan hasil estimasi terbaik antar metode GWR dan GTWR yang diterapkan pada data indeks pembangunan manusia di Provinsi Bengkulu Tahun 2018-2022. Terdapat tiga variabel yang dimodelkan, yakni tiga variabel bebas: angka harapan hidup, rata-rata lama sekolah, dan tingkat pengangguran terbuka, sedangkan variabel takbebas adalah Indeks Pembangunan Manusia. Hasil penelitian menunjukkan bahwa ketiga variabel bebas tersebut mempengaruhi variabel takbebas secara signifikan dan terdapat sifat heterogenitas spasial pada data yang dimodelkan. Sebagai tambahan, nilai koefisien determinasi untuk GTWR sebesar 99.98%, sedangkan untuk GWR sebesar 99.74%, jadi metode GTWR lebih baik untuk memodelkan Indeks Pembangunan Manusia di Provinsi Bengkulu tahun 2018-2022.
Co-Authors Cinta Rizki Oktarina Fachri Faisal Jose Rizal Pratiwi, Stevy Cahya