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All Journal Jurnal Ekonomi Pembangunan Journal of Innovation and Applied Technology JFMR (Journal of Fisheries and Marine Research) Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Varian Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Journal of Applied Data Sciences Abdi Geomedisains JURNAL PENGABDIAN PENDIDIKAN MASYARAKAT (JPPM)
Evellin Dewi Lusiana
Universitas Brawijaya
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Using Machine Learning Approach to Cluster Marine Environmental Features of Lesser Sunda Island Evellin Dewi Lusiana; Suci Astutik; Nurjannah Nurjannah; Abu Bakar Sambah
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.478

Abstract

Mapping marine ecosystems is acknowledged as a vital tool for implementing ecosystem services in practical situations. It provides a framework for effective marine spatial planning, enabling the designation of marine protected areas (MPAs) that consider ecological connectivity and habitat requirements. It also helps pinpoint areas of high biodiversity or ecological significance, allowing conservationists to prioritize these regions for protection and management. Numerous studies over decades have produced a vast amount of data that illustrates the features of the marine ecosystem. Therefore, the unsupervised learning is a promising technique to map marine ecosystem based on its environmental features. This study aims to compare unsupervised learning techniques to analyze marine environmental features in order to map marine ecosystem in Lesser Sunda waters. Eleven global environmental variables were accessed from global databases. The Lesser Sunda waters were delineated into groups according to their environmental characteristics using four unsupervised learning techniques: k-mean, fuzzy c-mean, self-organizing map (SOM), and density-based spatial clustering of applications with noise (DBSCAN). According to the findings, the Lesser Sunda waters can be divided into five to nine clusters, each with distinct environmental features. Moreover, the fuzzy c-mean method's clustering result outperformed the others based on the highest Silhouette (0.2204478) and Calinski-Harabasz (1741.099) Index. As an unsupervised learning technique, fuzzy c-mean clustering offered good performance in delineating Lesser Sunda Island marine waters with five clusters. The clustering results mostly consistent with existing conservation programs, even though there are several areas which needed international and multinational organization collaboration to effectively accomplish marine conservation objectives.
Co-Authors Abu Bakar Sambah Aminuddin Afandi Aminudin Afandhi Anugrah Fakhri Fadhlullah Mahmuda Arief Darmawan Celia Sianipar Dani Anggraini Dwi Ayu Lusia Iren Ana Tasya Kamelia Hidayat Mawadah Ikah Hamidah Meilina Retno Hapsari Mohammad Mahmudi Mudjiono Muhammad Musa Muhammad Musa Nanik Retno Buwono Nur Aliya Nabila Zsalzsabil Nurjannah Nurjannah Rafli Wildan Ramadiansyah Rakhel Marsyanda Vanny Alvionita Riski Pebri Sandikah Sarah Hutahaean Septi Nafisa Ulluya Zahra Siti Nurchotipah Solimun Suci Astutik Sulastri Arsad Sulastri Arsad Syahril Ramadhan Tisya W. Nisya Vella Ananda Sari Wahyudi Arif