Penggunaan Iso Cluster Unsupervised Classification Dalam Mengenali Garis Pantai, Studi Kasus: Rarowatu Utara, Sulawesi Tenggara

Abstract

Natural or anthropogenic activities often result in changes in natural conditions, in particular, changes in the coastline can have a sistem informasi geografinificant effect on the socio-economic effects of humans who are bound therein. Moreover, the coast is the earth's surface which is always experiencing dynamic changes. This study focuses on knowing how the influence of variability, area, and linearity of the coast in influencing the Iso Cluster Unsupervised Classification machine learning method which has high accuracy in extracting shoreline information. Satellite image data used is Landsat 8 ETM imagery, with observation times in the range 2013-2020. The use of this approach serves to increase effectiveness and efficiency and reduce the required resources. A case study was conducted in Southeast Sulawesi. Where the coastline in North Rarowatu District has been extracted. This shows that the Iso Cluster Unsupervised Classification can work and produce output as expected by making the necessary customizations. It is noted that the variability, area, and physical linearity of the coast affect the process of extracting information from the shoreline.