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Land use/cover greatly affect the quality of an area. Therefore, many regional planners need assistance byother fields, such as geoinformatics, computer science, environment, and others. Although prediction and forecasting have been widely studied, in regardto real conditions (geospatial)itstill needmoredevelopment, especially thoseinvolving a combination of regional types, such as urban and suburban areas. This study uses a remote sensing base and geographic information system in predicting land in the city and district of Bekasi, West Java, Indonesia. With two scenarios compared (business as usual and vegetation conservation), the model that has been created and validated (with an AUC accuracy result of 0.828) is used to predict land use change until 2030. Scenarios with vegetation conservation are able to keep green areas to switch to land types others, such as buildings and industry


Driving Factors Land Use Classification Markov Chain Multilayer Perceptron Neural Network

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