Pemetaan Ekosistem Mangrove Menggunakan Unsupervised Learning dengan Data Remote Sensing - Yesica Simangunsong; Winsy Christo Deilan Weku; Marline Sofiana Paendong (Indonesian Journal of Intelligence Data Science, 2024)
The Mangrove Ecosystem is one of the coastal ecosystems that experiences a lot of threats from various activities. The latest land use and land cover information is very necessary in regional development planning and environmental monitoring. One way to obtain this information is through remote sensing satellite image data processing. Therefore, this research aims to identify changes in mangrove forests using Machine Learning algorithms, namely K-Means and Random Forest, as well as determine the ability of the K-Means and Random Forest algorithms in identifying these land changes. The results of land cover changes in the mangrove ecosystem of Palaes Village in 2013 and 2021 based on Unsupervised Learning using the K-Means and Random Forest algorithms were clustered into 4 and 5 land cover classes based on different color classes, namely mangrove, sea water, other plants, raised sand and soil . Selection of the number of clusters is very important to get accurate results. The cluster results show that the clustering of the Palaes Village mangrove ecosystem looks more accurate on the 5 cluster K-Means map.