Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Economics Development Analysis Journal
Abdila, Naufal Shela
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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

Found 2 Documents
Search

 1 
Spatial Autocorrelation of East Java's Economic Growth Using Cluster-Based Weights Fitriani, Rahma; Wardhani, Ni Wayan Surya; Abdila, Naufal Shela
Economics Development Analysis Journal Vol. 13 No. 4 (2024): Economics Development Analysis Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edaj.v13i4.19161

Abstract

This study incorporates a spatial clustering technique into the formation of a spatial weight matrix as an alternative to the traditional exogenous matrix, aiming to better capture spatial dependencies. The approach is applied to analyze the spatial autocorrelation of economic growth in East Java’s regencies and municipalities using 2019–2021 data. Spatial clusters are identified based on GDP growth (GGDP), Human Development Index (HDI), population density (Dens), and geographical coordinates. These clusters are used to define a customized spatial weight matrix, where regions within the same cluster are designated as neighbors. Moran’s I, calculated using the customized spatial weight matrix, detects significant spatial autocorrelation in GDP growth for all three years, with consistently lower p-values compared to the traditional contiguity-based matrix. For example, in 2020, Moran’s I using the customized matrix yielded a p-value of 0.099 (significant at the 10% level), while the traditional matrix produced a non-significant p-value of 0.7965. These results demonstrate that spatial clustering extends the scope of spatial interaction beyond adjacent regions to include those with similar characteristics. The findings highlight the effectiveness of this method in providing a more nuanced and robust framework for analyzing spatial dependencies in economic growth.
CLUSTERING WITH SKATER METHODS AND UTILIZATION OF LISA ON UNEMPLOYMENT RATE Abdila, Naufal Shela; Fitriani, Rahma; Pratama, Muhamad Liswansyah
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2633-2646

Abstract

Spatial cluster analysis is an analysis used to identify a spatial pattern or geographical grouping of data. One method that can be used in spatial cluster analysis is Spatial Cluster Analysis by Tree Edge Removal (SKATER). This research aims to analyze the spatial pattern of the Unemployment Rate in East Java by utilizing the SKATER method. The clustering results are then used to create a weighting matrix, which is used to find local spatial autocorrelation values ​​using the Local Indicators of Spatial Association (LISA) index. The data is taken from BPS East Java with variables including unemployment rate, education level, minimum wage, Human Development Index, and population density. The results show that this approach is able to identify significant local spatial patterns. However, the selection of the number of clusters and input variables proved to be very influential on the results, so care needs to be taken.
Co-Authors Ni Wayan Surya Wardhani Pratama, Muhamad Liswansyah Rahma Fitriani