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Iksal Yanuarsyah
Universitas Ibn Khaldun Bogor
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Social Sciences Transportation Other

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Journal : Jambura Geoscience Review

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Machine Learning XGBoost Method for Detecting Mangrove Cover Using Unmanned Aerial Vehicle Imagery Minati Minati; Iksal Yanuarsyah; Sahid Agustian Hudjimartsu
Jambura Geoscience Review Vol 5, No 2 (2023): Jambura Geoscience Review (JGEOSREV)
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jgeosrev.v5i2.20782

Abstract

The mangrove ecosystem can be understood as a unique and different type of ecosystem that can benefit the surrounding ecosystem from the socio-economic and ecological perspective. The purpose of this study is to classify mangrove cover in Tanjung Lapin Beach, about 18.3 hectares, North Rupat District Bengkalis Regency, Riau Province, by applying machine learning XGBoost methods of UAV images by producing interpretations of mangrove cover in the research area. The use of machine learning with a high level of accuracy resulting from the XGBoost method is expected to help the availability of spatial data in identifying better mangrove forest cover. The data obtained from the orthomosaic results from the 3,500 tiles image is used as a reference for making sample points for the analysis process using the XGBoost method, with 224 sample points of mangrove objects visually recognized as training data. Regarding training data, the XGBoost method's iteration result obtained 99% overall accuracy and Kappa accuracy of about 0.98. It means the analysis process continues to the mangrove object cover detection stage. Based on the detection results, it was obtained about 11.9 hectares of mangrove forest cover (64% of the total study area). It has 68 sample points as test data used as an accuracy test tool from the detection results of mangrove objects, where an overall accuracy of 87% and kappa accuracy of 0.82 were obtained. This shows the successful use of the XGBoost method in identifying the mangrove's cover.
Spatial Analysis Model For Landslide Detection Using Relative Different NDVI (rdNDVI) Method Thought The Google Earth Engine Platform (Case Study: Sukajaya District, Bogor Regency) Nazar, Muarief Ahlun; Hermawan, Erwin; Yanuarsyah, Iksal
Jambura Geoscience Review Vol 6, No 2 (2024): Jambura Geoscience Review (JGEOSREV)
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jgeosrev.v6i2.23962

Abstract

This research utilizes the Google Earth Engine platform to detect landslides with relatively different NDVI (rdNDVI) methods. The purpose of the research is to improve our understanding of landslide analysis and detection, particularly those that occurred in Sukajaya district, Bogor Regency, Indonesia, on January 1, 2020. This research identifies vegetation changes associated with landslide likelihood using Sentinel-2A satellite image data available on Google Earth Engine. The results show that the rdNDVI method is effective in detecting landslides and can be used to determine areas that may be affected by landslides. This research also evaluates the accuracy of landslide detection by determining the threshold value to determine which areas are affected by landslides, by applying different slope values, the slopes used are slope 10, 15, 20, and 25. Comparing each slope results in a slope of 10 percent and a slope of 15 percent with 90% accuracy making the best accuracy compared to other slopes. The results of this research are expected to help the Regional Disaster Management Agency (BPBD) of Bogor Regency in managing landslides by conducting a careful and accurate analysis of areas that may be affected by landslides.
Co-Authors Ade Yansyah Andry Liu, Wahyu Dewi Primasari Disti Ayu Sadewa Erwin Hermawan Fety Fatimah Freza Riana Gibtha Fitri Laxmi Hadjimartsu, Sahid Agustian Iqbal, Reyhan Hikmatul Jie, Lie Jixiong, Cai Khalid, Nurkaliza Lulu Lutfi Latifah Minati Minati Nazar, Muarief Ahlun Nitin, Mahon Nurul Kamilah Purnomo, Muhammad Risza Puspa Eosina R, Muh Rafli Rahmatullah, Andhika Risza Purnomo, Muhammad Safaruddin Hidayat Al Ikhsan Sahid Agustian Hudjimartsu Sakti, Fikri Stephanie, Endah Nadiya Suwarno, Yatin Syawaludin Alisyahbana Harahap Tjahjo Tri Hartono Tuharea, Jumiati Yudi Setiawan