Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Indonesian Journal of Geography
Bella Rahmalia
Department of Geodesy and Geomatics Engineering, Faculty of Engineering, Universitas Lampung, Bandar Lampung, Indonesia
Earth & Planetary Sciences

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Integrating Machine Learning and Deep Learning to Predict Settlement Land Use Change and Carrying Capacity: A Case Study of Metro City, Indonesia Anggun Tridawati; Fajriyanto Fajriyanto; Armijon Armijon; Tika Christy N; Bella Rahmalia; Soni Darmawan
Indonesian Journal of Geography Vol 58, No 2 (2026): In Press
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.112398

Abstract

Rapid population growth and urban expansion in Metro City, Lampung Province, Indonesia, have intensified pressure on land resources and environmental sustainability. Therefore, this study aimed to integrate machine learning (Support Vector Machine, SVM) and deep learning (Cellular Automata–Artificial Neural Network, CA–ANN) to analyze as well as predict settlement land-use changes and assess land carrying capacity through 2038. SPOT satellite imagery from 2013, 2018, and 2023 was used for land cover classification. The results showed that settlement areas expanded from 1,087.16 ha in 2013 to 2,700.23 ha in 2023 and are projected to reach 4,336.22 ha by 2038, primarily driven by population growth and improved accessibility. The land carrying-capacity index ranged from 3.15 to 11.09, indicating that all districts remain above the minimum threshold (DDPm > 1), suggesting sufficient land availability to support projected settlement demand through 2038. Overall, the integration of SVM and CA–ANN proved effective for modeling complex urban dynamics and predicting future settlement changes. In conclusion, the results provide a scientific foundation for policymakers and urban planners to design data-driven and sustainable spatial development strategies in rapidly growing secondary cities.Received: 2025-10-23 Revised: 2026-05-04 Accepted: 2026-06-09 Published: 2026-08-05 
Co-Authors Anggun Tridawati Armijon Armijon Fajriyanto Fajriyanto Soni Darmawan Tika Christy N