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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Limits: Journal of Mathematics and Its Applications
Khamila, Azzahra Dhisa
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Computer Science & IT Mathematics

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Variables that Influence Urban Sprawl in DKI Jakarta, West Java and Banten Provinces in 2020 Khamila, Azzahra Dhisa; Sirait, Timbang
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 1 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 1 Edisi Ma
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i1.3372

Abstract

DKI Jakarta, West Java and Banten provinces are the place of two large metropolitan areas in Indonesia that are interconnected. As a result, these areas have a high level of urbanization which can lead to urban sprawl. Urban Sprawl can cause various negative impacts, especially on the environment. Therefore, it is necessary to minimize urban sprawl, one of many ways is by analyzing the variables that affect urban sprawl. Several studies on spatial analysis of urban sprawl have been made extensively using satellite imagery data, one of them states that NDBI can capture patterns, characteristics and the causes of urban sprawl. However, research that utilizes NDBI as a variable approach for the urban sprawl has never been conducted in Indonesia. Therefore, this research was conducted with the aim of analyzing the effect of variables that indicated influence urban sprawl in the provinces of DKI Jakarta, West Java and Banten using spatial analysis. The results show that the average NDBI value is high in urban areas where the majority are in DKI Jakarta province. The variables that significantly influence urban sprawl are percentage of migrant population and tertiary sector of GRDP. By focusing on these variables, the government can make policies to minimize and control urban sprawl that occur in their area.
The Use of Satellite Imagery Data for Poverty Clustering at the District Level Administration in Indonesia Khamila, Azzahra Dhisa; Wardani, Martha Budi; Kurniawan, Robert
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.25278

Abstract

Poverty is a problem that will never be separated from every country, including Indonesia. One of the efforts that can be taken to reduce poverty is to carry out comprehensive monitoring of data related to poverty. The use of satellite imagery strongly supports this effort. Data taken to describe poverty in a region are CO, SO2, NO2, Night Time Light (NTL), Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), also per capita expenditure data that can be accessed through the BPS website. Based on the theory, all of these variables negatively affect the poverty of a region except for the NDVI variable. The use of clustering with K-Means method can be implemented in this situation in order to cluster poverty in every district in Indonesia. Then it is supported by a descriptive analysis of each variable in order to describe the distribution of variables in each district in Indonesia. Based on the clustering results, it can be seen that there are 2 clusters, namely cluster 1 which shows a cluster with low poverty and cluster 2 with high poverty. There are a total of 46 districts included in cluster 1, which constitute the majority of economic centers in it's region, and 468 other districts included in cluster 2. The results of this clustering are expected to be used by stakeholders in making decisions according to the characteristics of the district.
Co-Authors Robert Kurniawan, Robert Sirait, Timbang Wardani, Martha Budi