This study by Virtriana et al 2025 looks at changes in the demand for healthcare facilities in West Java by 2030. It uses both static and dynamic data processed at 30×30 metre intervals across West Java. Dynamic parameter extrapolation uses data from 2000 to 2018 using random forest machine learning. The results show changing need and identifies suitable locations for healthcare facility sites.

This study by Virtriana et al 2025 looks at changes in the demand for healthcare facilities in West Java by 2030. Healthcare facilities must be proportional to the population in a given area.

It uses both static and dynamic data processed at 30×30 metre intervals across West Java. Dynamic parameter extrapolation uses data from 2000 to 2018 using random forest machine learning. The results show changing need and identifies suitable locations for healthcare facility sites.

In this study, a predictive model for the suitability of health facility locations in West Java for the year 2030 was developed using machine learning random forest. To obtain the suitability values, dynamic analyses of changes from 2018 to 2030 were conducted, based on land cover, population, LST, and AOD data. The results show that in West Java, built-up areas and dry land farming will increase in 2030. Meanwhile, land cover for forest, savanna, plantation, wet land farming, and water bodies will decrease. These land cover changes affect the suitability level of land for health facilities.

A comparison of the suitability results with existing health facilities was conducted, where a value of 0 represents very unsuitable and a value of 2 represents very suitable. The results show that the range for small health facilities is between 1.19 and 1.48, while for big health facilities, it is between 1.242 and 1.485. In the future this study identifies locations that consistently remain in suitable classes, ensuring their sustainability as health facility sites.

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