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All Journal Journal of Global Sustainable Agriculture Journal of Global Sustainable Agriculture
Eka Altiarika
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%).
Analysis of Seabed Ecosystem Zonation Using Remote Sensing to Support Conservation Strategies on Ketawai Island, Central Bangka: Analisis Zonasi Ekosistem Dasar Laut Berbasis Penginderaan Jauh untuk Mendukung Strategi Konservasi di Pulau Ketawai, Bangka Tengah Fifin Fitriana; Eka Altiarika
Journal of Global Sustainable Agriculture Vol 5, No 2 (July 2025)
Publisher : Universitas Muhammadiyah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32502/jgsa.v5i2.894

Abstract

Seabed ecosystems such as coral reefs and seagrass beds play a vital role in maintaining ecological balance and supporting the livelihoods of coastal communities. Ketawai Island, located in Central Bangka, harbors rich benthic ecosystems that are currently under pressure from anthropogenic activities such as tin mining, unregulated tourism, and pollution. This study aims to analyze the condition of seabed habitats around Ketawai Island and to develop a conservation zoning framework using a spatial approach. The data used includes Sentinel-2A satellite imagery and 140 sample points analyzed using the Support Vector Machine (SVM) algorithm. The classification resulted in four substrate classes: rubble/dead coral (39.06%), live coral reefs (34.95%), sand (24.68%), and seagrass (1.31%), with a classification accuracy of 85%, validated using a confusion matrix based on 140 reference points, divided into 70% for training and 30% for validation. The conservation zoning was divided into four categories: core zone (providing full protection for healthy coral and seagrass habitats), rehabilitation zone (for ecosystem restoration), limited-use zone (for educational tourism and research under strict regulation), and buffer zone (as a transitional area to mitigate external pressure and support the sustainability of other zones). This study recommends the protection of remaining coral reefs and seagrass areas, along with restoration efforts in degraded regions. The proposed zoning provides a scientific basis for sustainable coastal management and serves as a strategic approach for adapting to climate change.
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%).
Analysis of Seabed Ecosystem Zonation Using Remote Sensing to Support Conservation Strategies on Ketawai Island, Central Bangka: Analisis Zonasi Ekosistem Dasar Laut Berbasis Penginderaan Jauh untuk Mendukung Strategi Konservasi di Pulau Ketawai, Bangka Tengah Fifin Fitriana; Eka Altiarika
Journal of Global Sustainable Agriculture Vol 5, No 2 (July 2025)
Publisher : Universitas Muhammadiyah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32502/jgsa.v5i2.894

Abstract

Seabed ecosystems such as coral reefs and seagrass beds play a vital role in maintaining ecological balance and supporting the livelihoods of coastal communities. Ketawai Island, located in Central Bangka, harbors rich benthic ecosystems that are currently under pressure from anthropogenic activities such as tin mining, unregulated tourism, and pollution. This study aims to analyze the condition of seabed habitats around Ketawai Island and to develop a conservation zoning framework using a spatial approach. The data used includes Sentinel-2A satellite imagery and 140 sample points analyzed using the Support Vector Machine (SVM) algorithm. The classification resulted in four substrate classes: rubble/dead coral (39.06%), live coral reefs (34.95%), sand (24.68%), and seagrass (1.31%), with a classification accuracy of 85%, validated using a confusion matrix based on 140 reference points, divided into 70% for training and 30% for validation. The conservation zoning was divided into four categories: core zone (providing full protection for healthy coral and seagrass habitats), rehabilitation zone (for ecosystem restoration), limited-use zone (for educational tourism and research under strict regulation), and buffer zone (as a transitional area to mitigate external pressure and support the sustainability of other zones). This study recommends the protection of remaining coral reefs and seagrass areas, along with restoration efforts in degraded regions. The proposed zoning provides a scientific basis for sustainable coastal management and serves as a strategic approach for adapting to climate change.
Co-Authors Fifin Fitriana Nurlaila Saadah Ririn Apriyani