Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Eurekamatika
Pratiwi, Stevy Cahya
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Transportation

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
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.
SPATIAL MODELING OF POVERTY IN BENGKULU PROVINCE WITH MIXED GEOGRAPHICALLY WEIGHTED REGRESSION Nugroho, Sigit; Rini, Dyah Setyo; Jomecho, Tommy; Oktarina, Cinta Rizky; Pratiwi, Stevy Cahya; Karuna, Elisabeth Evelin
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 2 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss2pp0759-0772

Abstract

The percentage of poor people in Bengkulu Province is high from year to year. The poverty rate in Bengkulu Province also tends to fluctuate. If there is a decrease in the poverty rate, the decrease is relatively small. Poverty in the regions of Bengkulu Province also varies from district to district, subdistrict, and village to village, because poverty data is spatial data that varies regionally. The diversity of poverty data in Bengkulu Province is influenced by spatial effects, namely spatial dependency and spatial heterogeneity. Spatial dependency occurs due to spatial error correlation in cross section data, while spatial heterogeneity occurs due to random area effects, which is the difference between one region and another. Therefore, classical methods are not qualified enough to analyze the resulting diversity. This research will model the poverty of each district/city in Bengkulu Province using Mixed Geographically Weighted Regression (MGWR), because this method is quite complex in modeling data that contains spatial heterogeneity and variations in geospatial data. This modeling aims to identify and analyze poverty indicators in Bengkulu Province spatially, namely based on poverty data in each district/city in Bengkulu Province. The results showed that by using the MGWR method, the variables that locally influence the percentage of extreme poor people in each district/city in Bengkulu Province are Female Household Head Gender and not having a waterheater . Meanwhile, the variable that has a global effect on the percentage of the extreme poor in each district/city in Bengkulu Province is not having a flat screen television ().
Co-Authors Cinta Rizki Oktarina Fachri Faisal Jomecho, Tommy Jose Rizal Karuna, Elisabeth Evelin Oktarina, Cinta Rizky Rini, Dyah Setyo Sigit Nugroho Tasyah, Qhiky Lioni