Prediction of Carbon Emissions in Indonesia Using Machine Learning: A Focus on Environmental Impact

Rizaldi Putra, Memet Sanjaya, Deni Utama, Berliana Berliana

Abstract


Carbon emissions represent a critical driver of global climate change, exerting profound impacts on environmental sustainability and public health. This research examines Indonesia's carbon emission trends using a comprehensive dataset spanning global emissions from 1960 to 2018, with specific focus on Indonesia, obtained from Kaggle. Employing Linear Regression (LR) as the primary machine learning technique, the study effectively models and forecasts future carbon emission levels for Indonesia. The findings indicate a projected increase in emissions to 2.38 tons per capita annually by 2030, underscoring the urgent need for robust environmental policies.

Keywords


Machine Learning; Carbon Emission; Indonesia; Linear Regression

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DOI: https://doi.org/10.31326/jisa.v7i2.2115

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JOURNAL IDENTITY

Journal Name: JISA (Jurnal Informatika dan Sains)
e-ISSN: 2614-8404, p-ISSN: 2776-3234
Publisher: Program Studi Teknik Informatika Universitas Trilogi
Publication Schedule: June and December 
Language: English
APC: The Journal Charges Fees for Publishing 
Indexing:  EBSCO , DOAJ, Google Scholar, Arsip Relawan Jurnal Indonesia, Directory of Research Journals Indexing, Index Copernicus International, PKP Index, Science and Technology Index (SINTA, S4) , Garuda Index
OAI address: http://trilogi.ac.id/journal/ks/index.php/JISA/oai
Contact: jisa@trilogi.ac.id
Sponsored by: DOI – Digital Object Identifier Crossref, Universitas Trilogi

In Collaboration With: Indonesian Artificial Intelligent Ecosystem(IAIE), Relawan Jurnal Indonesia, Jurnal Teknologi dan Sistem Komputer (JTSiskom)

 

 

JISA (Jurnal Informatika dan Sains) is Published by Program Studi Teknik Informatika, Universitas Trilogi under Creative Commons Attribution-ShareAlike 4.0 International License.