Implementing Support Vector Machine Algorithm for Early Slum Identification in Yogyakarta City, Indonesia Using Pleiades Images

Slums are one of the urban problems that continue to get the attention of the government and the city of Yogyakarta. Over time, cities continue to experience changes in land use due to population growth and migration. Therefore, it is necessary to monitor the existence of slums continuously. The objectives of this study are to conduct early identification of the slum using the Support Vector Machine (SVM) Algorithm, which is applied to the Pleiades Image in parts of Yogyakarta City, to test the accuracy of the slum mapping results generated from the SVM compared to the Slum Map of the KOTAKU Program. The data used are Pleiades Image, administrative maps, and existing slum maps of the KOTAKU Program, which are used to test the accuracy. The method used is Machine Learning with a Support Vector Machine Algorithm. The parameters used for early identification of the slums are the characteristics of the object (characteristics of buildings), settlement (density and shape), and the environment (location and its proximity to rivers and industries). We separate slum and non-slum based on texture, morphology, and spectral approaches. Based on the accuracy test results between the SVM classification results map of the slum and the map from the KOTAKU Program, the accuracy is 86.25% with a kappa coefficient of 0.796

The Compatibility Study of Sentinel 1 Multitemporal Analysis For River-Flood Detection, Study Case: Bogowonto River

Flooding is a common natural disaster in Purworejo District, which can be caused by the overflowing of the Bogowonto River. The use of multitemporal analysis with Synthetic Aperture Radar (SAR) images, such as Sentinel-1, has the potential to aid in flood inundation detection for disaster mitigation in the area. However, there has not been any research examining the compatibility of flood inundation detection using multitemporal Sentinel-1 images with the flood susceptibility characteristics of the Bogowonto River. This study aims to evaluate this using a SWOT analysis. The results show that multitemporal analysis using Sentinel-1 images is not suitable for detecting flood inundation in the Bogowonto River due to difficulties in finding the right acquisition time at the time of the flood event. The duration of floods in the Bogowonto River is approximately 1-2 days, while the earliest reacquisition time for Sentinel-1 images for this study is 12 days. Additionally, Sentinel-1 images using band C have limitations in detecting floods under vegetation