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All Journal Journal of Global Sustainable Agriculture Journal of Global Sustainable Agriculture
Nurlaila Saadah
Program Studi Ilmu Komputer, Fakultas Teknik dan Sains, Universitas Muhammadiyah Bangka Belitung, Pangkalpinang, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Earth & Planetary Sciences Environmental Science

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Identification of Tin Mining Land Cover Change in Central Bangka Regency Using Machine Learning dan Google Earth Engine (GEE) Fifin Fitriana; Eka Altiarika; Ririn Apriyani; Nurlaila Saadah
Journal of Global Sustainable Agriculture Vol 5, No 1 (December 2024)
Publisher : Universitas Muhammadiyah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32502/jgsa.v5i1.333

Abstract

Information on changes in the area of tin mining land is crucial as a basis for rehabilitation efforts to support environmental sustainability. Bangka Tengah Regency, located in Bangka Belitung Province, experiences high levels of tin mining activity, posing significant environmental damage risks. This is particularly concerning when mining is carried out by unconventional tin miners (TI), most of whom lack environmental permits, leaving abandoned mining pits without rehabilitation efforts. Consequently, the area of mining land expands each year, exacerbated by surges in tin prices. Mining activities may even spread to residential areas, endangering communities through metal contamination, sedimentation, and soil erosion. This study aims to analyze changes in tin mining land from 2013 to 2022 and map the distribution of tin mining areas in Bangka Tengah Regency. The analysis integrates remote sensing with Machine Learning techniques. The CART (Classification and Regression Trees) algorithm is employed for the classification of mining land changes, with analysis conducted on the Google Earth Engine (GEE) platform. The study reveals that the tin mining land area in Bangka Tengah increased from 132.19 km² in 2013 to 207.62 km² in 2022, reflecting an increase of 75.42 km² (3.58%).
Identification of Tin Mining Land Cover Change in Central Bangka Regency Using Machine Learning dan Google Earth Engine (GEE) Fifin Fitriana; Eka Altiarika; Ririn Apriyani; Nurlaila Saadah
Journal of Global Sustainable Agriculture Vol 5, No 1 (December 2024)
Publisher : Universitas Muhammadiyah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32502/jgsa.v5i1.333

Abstract

Information on changes in the area of tin mining land is crucial as a basis for rehabilitation efforts to support environmental sustainability. Bangka Tengah Regency, located in Bangka Belitung Province, experiences high levels of tin mining activity, posing significant environmental damage risks. This is particularly concerning when mining is carried out by unconventional tin miners (TI), most of whom lack environmental permits, leaving abandoned mining pits without rehabilitation efforts. Consequently, the area of mining land expands each year, exacerbated by surges in tin prices. Mining activities may even spread to residential areas, endangering communities through metal contamination, sedimentation, and soil erosion. This study aims to analyze changes in tin mining land from 2013 to 2022 and map the distribution of tin mining areas in Bangka Tengah Regency. The analysis integrates remote sensing with Machine Learning techniques. The CART (Classification and Regression Trees) algorithm is employed for the classification of mining land changes, with analysis conducted on the Google Earth Engine (GEE) platform. The study reveals that the tin mining land area in Bangka Tengah increased from 132.19 km² in 2013 to 207.62 km² in 2022, reflecting an increase of 75.42 km² (3.58%).
Co-Authors Eka Altiarika Fifin Fitriana Ririn Apriyani