Evaluating the Impact of Ibu Kota Nusantara (IKN) Development on Land Cover Using Machine Learning-Based Sentinel-2A Satellite Image Classification

Abstract

The development of Ibu Kota Nusantara (IKN) in East Kalimantan as Indonesia's new capital city has the potential to cause significant changes to land cover patterns, especially in tropical rainforest areas. This study aims to evaluate the impact of IKN development on land cover using Sentinel-2A satellite image data and a machine learning approach. The study area is focused on the IKN Core Urban Area by comparing land cover conditions in 2022 before development and 2024 after development. Three classification methods were used including Random Forest, Support Vector Machines, and Classification and Regression Trees. The results showed that the RF model had the best accuracy with an overall accuracy value above 93% in both time periods. Spatial analysis showed a decrease in vegetation area and an increase in open land as an indication of intensive land clearing activities. These findings emphasize the importance of continuous land cover monitoring to support IKN's vision as a green city and achieve sustainable development targets (SDGs 11 and 15). This research is expected to serve as a reference for the formulation of adaptive and environmentally friendly spatial policies.