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All Journal Jurnal Agronomi Indonesia (Indonesian Journal of Agronomy) Jurnal Ilmu Pertanian Indonesia Jurnal Hortikultura Indonesia (JHI) Biotika: Jurnal Ilmiah Biologi AGRIEKONOMIKA Agrovigor: Jurnal Agroekoteknologi REKAYASA Jurnal Agro Jurnal Wilayah dan Lingkungan Jurnal Ilmiah Pangabdhi Network Engineering Research Operation [NERO] Jurnal Teknik Elektro dan Komputer TRIAC Natural Science Education Research (NSER)
EKO SETIAWAN
Universitas Trunojoyo Madura
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Immunology & microbiology Physics Social Sciences Other

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MANGROVE HEALTH INDEX IN MARTAJASAH MANGROVE ECOTOURISM, BANGKALAN REGENCY, EAST JAVA, INDONESIA Akhmad Farid; Eko Setiawan; Apri Arisandi; Moch. Yusron
Jurnal Wilayah dan Lingkungan Vol 12, No 2 (2024): Agustus 2024
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jwl.12.2.150-162

Abstract

Mangrove areas need to be considered from the condition of distribution and density of mangrove forests for future mangrove forest management planning, especially on Madura Island, especially Martajasah mangrove ecotourism. The purpose of this study is to determine the type of mangrove, canopy cover, and mangrove health index. The method used in this study is observation, which is to know the general overview of the research to be carried out. The selection of the research location was determined by the stratified random sampling method. The canopy cover on the mangrove was measured using the Hemispherical Photography method using the front camera of the OPPO Reno 6 phone with a camera resolution of 8 megapixels with a 180° angle of view at the point of taking the photo. The results of this study found 7 types of mangroves, including Sonneratia alba, Sonneratia caseolaris, Rhizophora apiculata, Rhizophora mucronata, Avicennia alba, Avicennia marina, and Lumnitzera racemosa. The percentage of canopy cover obtained an average score of 63.44% or in the solid category. This indicates that the mangrove ecosystem found in Martajasah mangrove ecotourism is in a good category. The Mangrove Health Index in Martajasah Mangrove Ecotourism is included in normal (33-66%).
Land Use Analysis Using Machine-Learning Based on Cloud Computing Platform Prasetyo, Syukur Toha; Rahman, Fahmi Arief; Suryawati, Sinar; Supriyadi, Slamet; Setiawan, Eko
Jurnal Ilmu Pertanian Indonesia Vol. 30 No. 4 (2025): Jurnal Ilmu Pertanian Indonesia
Publisher : Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18343/jipi.30.4.765

Abstract

Land use analysis can provide a foundation for successful and efficient regional planning and environmental monitoring. The application of machine-learning on a cloud computing platform (Google Earth Engine, GEE) in land use analysis enables efficient and rapid processing of spatial data on a wide scale. It overcomes the constraints inherent in conventional approaches. The purpose of this study was to identify land use and estimate its level of accuracy using GEE and a Random Forest machine-learning method. The data utilized were the administrative boundaries of Bangkalan Regency (1:25,000) and Landsat 8 SR L2 C2 T1 satellite images from 2022. Satellite image analysis using the Random Forest algorithm on the GEE platform with the JavaScript API, including masking, cloud masking, class and sampling, training, and testing sample data. Land use study using the Random Forest algorithm yielded the following results in order of area: vegetation 65,040.39 ha (49.98%), agricultural land 31,817.16 ha (24.45%), settlements 20,578.05 ha (15.81%), open land 6,683.94 ha (5.14%), and water bodies 6,021.09 ha (4.63%). The accuracy test in GEE revealed an overall accuracy (OA) of 91.39% and a kappa score of 88.39%, or 0.88. At the same time, validation in the field gave an OA of 88.68% and a Kappa of 85.53%. The findings of this study can be applied to land use evaluation and fundamental decision-making. Keywords: land use, random forest, geographic information system, remote sensing
Co-Authors Achmad Ghoni Yuliyanto Aeri Rachmad Akhmad Farid Andrie Kisroh Sunyigono Anggi Nor Fauziah Apri Arisandi Arifin Surya Dwipa Irsyam Arwinda Mifta Zulfida Ashari Bagus Setiawan Atria, Mega Aulya Fridayanti Badami, Kaswan Bain Khusnul Khotimah Bain Khusnul Khotimah Fahmi Arief Rahman Fumio Fukuda Hasbullah, Abdul Wahib Ikbar Maulana Kaswan Badami Kuniyuki Saitoh Lilis Udkhulul Jannah Mariya Ulfa Moch. Yusron Muhammad Rifqi Hariri Naohiro Kubota Nobuo Sugiyama Prasetyo, Syukur Toha Rina Ratnasih Irwanto Roedhy Poerwanto Septin Puji Astuti Setiani Setiani Sinar Suryawati Slamet Supriyadi Subhan - Suhartono, Suhartono Verdi Sasmeka Wen-Chi Huang Wulan Al Fitra