Prediksi Kerentanan Kekeringan Perkotaan Menggunakan Machine Learning: Pendekatan untuk Perencanaan Kota Tangguh di Kota Kupang - Subnafeu, Eglantyne Lidya; Reza, Mohammad; Gai, Ardiyanto Maksimilianus; Afrianto, Firman (Jurnal Teknologi Sistem Informasi dan Aplikasi, 2025)
The city of Kupang in East Nusa Tenggara faces increasingly serious drought challenges due to climate change and rapid urbanization, prompting the need for this study to predict drought vulnerability in the region. This study aims to develop a drought vulnerability prediction model using a machine learning approach that combines Normalized Difference Drought Index (NDDI) data, built-up areas, and population data in the last five (5) years. The methods used include NDDI calculations from satellite imagery, a zonal statistical analysis, and drought vulnerability simulations using models such as Random Forest, Support Vector Machine, and Artificial Neural Network. The results show that the Random Forest model provides the best prediction with the highest R Squared value, and indicates an increased risk of drought in several villages in 2030 and 2040, especially in areas with rapid population growth and expansion of built-up areas. The conclusion of this study confirms that the developed approach is able to provide a more accurate picture of drought-prone areas, so that it can be an important guide in more resilient and sustainable urban  planning, and recommends strengthening water management and spatial planning policies with early intervention in the most vulnerable areas.