[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127170-en":3,"doc-seo-127170-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127170,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Evaluating the Impact of Agricultural Technology on Greenhouse Gas Emissions Using Machine Learning","Agriculture drives global warming through emissions of methane (CH4) and nitrous oxide (N2O), which carry much higher global warming potential than carbon dioxide (CO2). This study evaluates how agricultural technological developments affect greenhouse gas emissions by applying machine learning models, focusing on XGBoost and Support Vector Machine (SVM). Emission data across multiple crops and farming technologies are used to train and assess predictive performance. Results indicate certain crops substantially increase emissions and some new technologies can intensify the problem. XGBoost reaches 99.6% accuracy, while SVM achieves 99.5%, supporting data-driven mitigation planning and technology-informed policy design.","Journal of Information Systems and Informatics  \nVol. 6, No. 4, December 2024 e-ISSN: 2656-4882 p-ISSN: 2656-5935  \nDOI: 10.51519/journalisi.v6i4.870 Published By DRPM-UBD  \nEvaluating the Impact of Agricultural Technology on Greenhouse Gas Emissions Using Machine Learning  \nEko Priyono1, Ispandi2, Rusdi3  \n1Computer Science Master's Study Program, Nusa Mandiri University, Jakarta, Indonesia  \n2,3 Universitas Bina Sarana Informatika, Jakarta, Indonesia  \n[Email:](Email:1 14220040@nusamandiri.ac.id)[1](Email:1 14220040@nusamandiri.ac.id)[ 14220040@nusamandiri.ac.id](Email:1 14220040@nusamandiri.ac.id), [2](2ispandi.ipd@bsi.ac.id)[ispandi.ipd@bsi.ac.id](2ispandi.ipd@bsi.ac.id), [3](3 rusdialfiantanjung01@gmail.com)[ rusdialfiantanjung01@gmail.com](3 rusdialfiantanjung01@gmail.com)  \nAbstract  \nAgriculture 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 technologydriven policies.  \nKeywords: Greenhouse Gas Emissions, Agriculture, XGBoost, Support Vector Machine (SVM), Agricultural Technolo  \n1. INTRODUCTION  \nAgriculture has a significant impact on greenhouse gas emissions, with a contribution that cannot be overlooked in global climate change [1],[2] . Greenhouse gas emissions coming from the farming industry, primarily stemming from conventional farming, plantations, and livestock farming, are a major concern in climate change mitigation efforts [3],[4],[5] . One of the principal sources of emissions of greenhouse gases from farming is livestock activities. Livestock produces manure, a major source of methane (CH4), one of the most environmentally damaging greenhouse gases. The process of making manure fertilizer from animal waste also contributes to greenhouse gas emissions, particularly nitrous oxide (N2O) . In addition, agricultural land management also contributes to greenhouse gas emissions [6],[7] .  \n2224  \nThis work is licensed under a Creative Commons Attribution 4.0 International License.  \np-ISSN: 2656-5935 [http://journal-isi.org/index.php/isi](http://journal-isi.org/index.php/isi) e-ISSN: 2656-4882  \nBurning agricultural residues, using chemical fertilizers, and applying lime to soil can all result in the emission of carbon dioxide and nitrogen oxides. These processes can increase the atmospheric concentration of greenhouse gasses [8],[9] . Surprisingly, rice cultivation is among the largest those who contribute to emissions of greenhouse gases from the farming sector, mainly utilizing the production of methane from the decay of organic matter in flooded rice fields [10] . In the context of Changes in climate mitigation, yes, it is essential to comprehend the differences amid generated greenhouse gasses by farming, such as Nitrous oxide, carbon dioxide, and methane. Measuring carbon emissions from agriculture and livestock farming is also a key consideration in developing effective environmental policies [11] .  \nRaising awareness of agriculture's impact on climate change, along with efforts to reduce greenhouse gas pollution from this sector, is crucial","cbCailQX5nXjDnfg","https://ap.wps.com/l/cbCailQX5nXjDnfg","pdf",879187,1,13,"English","en",105,"# Abstract\n# Keywords\n# 1. 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