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All Journal Jurnal Ilmu dan Teknologi Kelautan Tropis
Muhammad Anshar Amran
Department of Marine Science, Faculty of Marine Science and Fisheries, Hasanuddin University, Makassar
Biochemistry, Genetics & Molecular Biology Decision Sciences, Operations Research & Management Library & Information Science

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KOMPARASI KLASIFIKASI TUTUPAN LAMUN BERDASARKAN ALGORITMA SVM DAN FUZZY MENGGUNAKAN CITRA MULTI-SKALA DI PULAU KODINGARENG LOMPO Anisa Aulia Sabilah; Vincentius Paulus Siregar; Muhammad Anshar Amran
Jurnal Ilmu dan Teknologi Kelautan Tropis Vol. 13 No. 1 (2021): Jurnal Ilmu dan Teknologi Kelautan Tropis
Publisher : Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jitkt.v13i1.34765

Abstract

Seagrass beds play an ecological role in the shallow marine environment, such as a habitat for biota, primary producers, and sediment traps. They also act as nutrient recyclers. Since they have such an important role, this natural resource needs to be preserved. Therefore, continuous monitoring and mapping of seagrass beds, especially by remote sensing methods, is paramount. The current rapid development of satellite sensor technology, especially its spatial and spectral resolutions, has improved the quality of the seagrass distribution map. The use of proper classification methods and schemes in the classification of seagrass distribution based on satellite imagery can affect the accuracy of the map, which is why various alternative algorithm studies are required. In this study, the Support Vector Machine and Fuzzy Logic algorithms were used to classify the WorldView-2 and Sentinel-2 satellite imageries on Kodingareng Lompo Island with four classes of seagrass cover, sparse (0–25%), moderate (26–50%), dense (51–75%), and very dense (76–100%). The result showed that the Fuzzy Logic algorithm applied to WorldView-2 imagery has the best overall accuracy of 78.60% seagrass cover classification.
Co-Authors Sabilah, Anisa Aulia Vincentius P Siregar