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All Journal Indonesian Journal of Intelligence Data Science (IJIDS)
Yesica Simangunsong
Unsrat
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Library & Information Science Mathematics

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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 Vol 3 No 1 (2024): Volume 3 No 1 2024
Publisher : Faculty of Mathematics and Natural Sciences Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/ijids.v3i1.50153

Abstract

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.
Co-Authors Marline Sofiana Paendong Winsy Christo Deilan Weku