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All Journal Ganendra: Majalah IPTEK Nuklir Jurnal Teknologi Pertanian MONETER JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Information Systems and Informatics Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Journal Medical Informatics Technology JURNAL ILMIAH GEMA PERENCANA The Indonesian Journal of Computer Science
Eko Priyono
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Medicine & Pharmacology Public Health Social Sciences Other

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Journal : Journal of Information Systems and Informatics

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Evaluating the Impact of Agricultural Technology on Greenhouse Gas Emissions Using Machine Learning Priyono, Eko; Ispandi, Ispandi; Rusdi, Rusdi
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.870

Abstract

Agriculture is a significant contributor to global warming, primarily due to the release of greenhouse gases like methane (CH4) and nitrous oxide (N2O). These gases have a much higher global warming potential than carbon dioxide (CO2), necessitating targeted strategies for their reporting and reduction. This study applies machine learning models, specifically XGBoost and Support Vector Machine (SVM), to evaluate how technological advancements in agriculture influence greenhouse gas emissions. The dataset used includes emission data from various crops and farming technologies. Findings reveal that certain crops considerably elevate emissions, and in some cases, new technologies exacerbate the issue. XGBoost achieved 99.6% accuracy in predicting emission mitigation, proving its effectiveness in developing climate change mitigation plans for agriculture. Support Vector Machine also performed well, with an accuracy of 99.5%. This research underscores the need for precise approaches in managing greenhouse gas emissions through technology-driven policies.
Co-Authors Apriyanto, Mulono Aziz, Faruq Fatah, Teddy Al Hendri Maulana Ispandi Ispandi, Ispandi Ma'mun, Sukrul Ma’mun, Sukrul Ninsix, Retti Retti Ninsix Rusdi Rusdi Syarip Syarip Yohannes Sardjono