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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmiah KOMPUTASI Jurnal Penelitian Pendidikan IPA (JPPIPA) Jurnal Sains Dan Teknologi (SAINTEKBU) Advance Sustainable Science, Engineering and Technology (ASSET)
Suharjito
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Physics

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Journal : Advance Sustainable Science, Engineering and Technology (ASSET)

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Boosting-Based Machine Learning Models and Hyperparameter Tuning for Predicting Vehicle Carbon Dioxide Emission Ridwan Petervan Siburian, Firman; Suharjito
Advance Sustainable Science Engineering and Technology Vol. 7 No. 4 (2025): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i4.2097

Abstract

Sustainable development and climate change are central agendas in global policy and research. This study examines and compares three ensemble learning models using Gradient Boosting Machine, Categorical Boosting, and Extreme Gradient Boosting for forecasting vehicle carbon dioxide (CO2) emissions. Data preprocessing with Interquartile Range (IQR) and median imputation is among the methods used to address missing values in CO₂ rating and smog rating variables. SHAP and PDP were employed for feature importance analysis and model interpretability. The findings from the third experiment demonstrate that Extreme Gradient Boosting (XGBoost) outperformed other models achieving a Coefficient Determination of 0.9988, Root-Mean-Square Error of 2.1696, Mean-Absolute Error of 0.4977, and Mean-Absolute-Percentage Error of 0.0019. The primary predictive features included combined fuel consumption (liters/100 km), city and highway fuel consumption, ethanol fuel consumption, model year, engine size and diesel consumption. The findings suggest the potential of boosting-based models for supporting sustainable transport planning, policy for emission reduction, and evidence-based policy making.
Co-Authors Ahmad Yusuf Al-Majid August, Ryan Alpha Budi Prasetyo Pieter, Franky Bryan Putro, Danar Adityo Ridwan Petervan Siburian, Firman