DOI: https://doi.org/10.14710/jwl.14.2.161-179

Prediction of Built-up Land Changes until 2032 Using the Cellular Automata-Artificial Neural Network (CA-ANN) and Logistic Regression Approaches (Case Study: Gerbangkertosusila)

Prastika Wulandari  -  UPN “Veteran” Yogyakarta, Jl. Tambak Bayan No.2, Janti, Caturtunggal, Kec. Depok, Kab. Sleman, Daerah Istimewa Yogyakarta, 55281, Indonesia, Indonesia
*Dwi Wahyuningrum orcid  -  UPN “Veteran” Yogyakarta, Jl. Tambak Bayan No.2, Janti, Caturtunggal, Kec. Depok, Kab. Sleman, Daerah Istimewa Yogyakarta, 55281, Indonesia, Indonesia
Danang Budi Susetyo  -  Badan Riset dan Inovasi Nasional, Jl. Raya Bogor No. 970, Nanggewer Mekar, Kec. Cibinong, Kab. Bogor, Jawa Barat, 16915, Indonesia, Indonesia

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Abstract
Urban areas are highly vulnerable to land cover changes, particularly the conversion of non-built-up land into built-up areas due to increasing population growth and urban activities. As the second-largest metropolitan agglomeration in Indonesia after Jabodetabek, Gerbangkertosusila has experienced rapid urbanization driven by migration and regional development. Understanding land-cover dynamics in this region is important for supporting sustainable urban planning and future land management. Therefore, this study aims to analyze built-up land expansion in Gerbangkertosusila from 2012 to 2022 and predict future growth in 2027 and 2032 using Cellular Automata–Artificial Neural Network (CA-ANN) and Logistic Regression models. The modeling process incorporated driving factors including proximity to roads, buildings, rivers, and Digital Elevation Model (DEM). The results indicate that built-up land increased from 41,417.50 hectares in 2012 to 63,928.06 hectares in 2022. The ANN model demonstrated higher accuracy (94.01% correctness, kappa 0.64) than Logistic Regression (93.09% correctness, kappa 0.58). Future projections show continued expansion, with the ANN model estimating 64,017.43 hectares in 2027 and 89,237.90 hectares in 2032, while the Logistic Regression model predicts 62,662.39 hectares in 2027 and 84,438.57 hectares in 2032. These findings indicate that Gerbangkertosusila will continue to experience significant urban expansion in the coming years. The projected spatial patterns for 2027 and 2032 can support urban spatial planning, infrastructure development, and strategies to mitigate uncontrolled urban expansion in Gerbangkertosusila.
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Keywords: CA-ANN; Changes in Built Land; Logistic Regression

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