Prediksi Konsumsi Bahan Bakar Menggunakan Machine Learning Berdasarkan Data ECU
DOI:
https://doi.org/10.55606/jcsr-politama.v4i4.6543Keywords:
ECU, Fuel Consumption, Machine Learning, Prediction, RegressionAbstract
Fuel consumption efficiency is a key indicator in the development of modern vehicles, as it directly affects operating costs and exhaust emissions. This study aimed to analyze the characteristics of Electronic Control Unit (ECU) data as predictive parameters for fuel consumption and to develop a regression-based Machine Learning model for predicting vehicle fuel consumption. The dataset consisted of 597 vehicle records, with Engine Size, Cylinders, CO₂ Emissions, and Fuel Type serving as predictor variables, while Combined Fuel Consumption was used as the target variable. Data were analyzed using multiple linear regression, and model performance was evaluated based on the coefficient of determination (R²), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Standard Error of Estimate (SEE). The results demonstrated that the proposed model achieved excellent predictive performance, with an R value of 0.989, an R² of 0.977, an adjusted R² of 0.977, an SEE of 0.432 L/100 km, an MAE of 0.24 L/100 km, and an RMSE of 0.430 L/100 km. Among the predictor variables, CO₂ Emissions was identified as the most influential factor affecting fuel consumption. These findings indicate that ECU data can provide accurate fuel consumption predictions and have significant potential to support the development of vehicle fuel-efficiency monitoring systems and data-driven decision-making in the automotive industry.
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References
Akram, A. (2026). Fuel Consumption Prediction: A Comparative Analysis of Machine Learning Paradigms. Computer Science, 1(1), 1–29. https://doi.org/10.48550/arXiv.2603.21034
Anitha, K., & Babu, R. (2026). Stochastic Modeling of Fuel Consumption : A Predictive Machine Learning Framework for Energy Analytics. Journal of Computational Analysis and Applications, 35(6), 10–18. https://doi.org/10.48047/jocaaa.2026.35.06.02
Arda, E., Demir, M., Çelebi, S., Demir, Ü., & Seçgin, Ö. (2026). Leveraging Advanced Machine Learning Algorithms for Energy Efficiency and Carbon Footprint Reduction in Diesel Engines. International Journal of Automotive Science and Technology, 10(1), 241–259. https://doi.org/10.309 39/ijastech..1896122
Canal, R., Riffel, F. K., & Gracioli, G. (2024). Machine learning for real-time fuel consumption prediction and driving profile classification based on ECU data. IEEE Access, 12, 1–16. https://doi.org/10.1109/ACCESS.2024.3400933
Deepthi, P., Nithya, M., Mounika, P., Mourya, K., Reddy, P. M. V., Ananth, C., & Rabbimovich, Y. O. (2026). Machine Learning for Fuel Consumption Prediction and Driving Profile Classification Based on ECU Data Next-Generation Engineering Aprendizaje automático para la predicción del consumo de combustible y la clasificación del perfil de conducción según datos. Superintelligence Series, 3, 275–290. https://doi.org/10.62486/978-9915-9851-0-7_202648
Guodong, S., Mingmao, H., Aihong, G., Qingshan, G., Qinghe, G., & Hao, T. (2025). Vehicle fuel consumption optimization prediction model based on XGBoost-MSIWOA-LSTM. Computer Integrated Manufacturing System, 31(9), 3467–3484. https://doi.org/10.13196/j.cims.2023.0232
Hazra, S., Tangadpalliwar, S., & Hazra, S. (2026). Data-Driven Prediction of Global Automotive Trends: Forecasting Fuel Economy and CO₂ Emissions Using Machine Learning. Symposium on International Automotive Technology (2026), 1–20. https://doi.org/10.4271/2026-26-0643
Ibrahim, R. A., & Zakzouk, N. E. (2026). A machine learning framework for predicting fuel consumption and CO2 emissions in hybrid and combustion vehicles: comparative analysis and performance evaluation. PLOS ONE, 21(2), e0342418. https://doi.org/10.1371/journal.pone.0342418
Jessica, F., Thomas, W. C., Zepani, C. L., Fadillah, M. E., & Annisa, R. (2025). Aplikasi Perkiraan Efisiensi Bahan Bakar Mobil dengan Machine Learning dan Streamlit. JUKOMIKA (Jurnal Ilmu Komputer Dan Informatika), 8(2), 86–92. https://doi.org/10.54650
Keskin, R., Belge, E., & Kutoglu, S. H. (2025). Optimizing Fuel Consumption Prediction Model Without an On-Board Diagnostic System in Deep Learning Frameworks. In Sensors (Vol. 25, Issue 22, p. 7031). https://doi.org/10.3390/s25227031
Ko, K. (2022). Machine Learning Methods to Predict Vehicle Fuel Consumption. Journal of The Korea Society of Computer and Information, 27(9), 13–20. https://doi.org/10.9708/jksci.2022.27.09.013
Kusnawi, Wibowo, M. A., & Sanjaya, R. (2026). Adaptive Gradient Boosting for Fuel Consumption Prediction in Mining Haul Trucks under Concept Drift Monitoring. Jurnal Teknik Informatika (JUTIF), 7(2), 1758–1777. https://doi.org/10.52436/1.jutif.2026.7.2.5635
Li, Y., Zeng, I. Y., Niu, Z., Shi, J., Wang, Z., & Guan, Z. (2022). Predicting vehicle fuel consumption based on multi-view deep neural network. Neurocomputing, 502, 140–147. https://doi.org/10.1016/j.neucom.2022.06.047
Liu, T. (2026). Advanced active learning and metaheuristic optimization framework for robust vehicle carbon footprint prediction using engine specifications and fuel efficiency metrics. Journal of Cleaner Production, 549, 147827. https://doi.org/10.1016/j.jclepro.2026.147827
Liu, X., & Jin, H. (2023). High-precision transient fuel consumption model based on support vector regression. Fuel, 338, 127368. https://doi.org/10.1016/j.fuel.2022.127368
Mariprasath, T., Srinivasan, P., Anbazhagan, G., Karuvelampalayam, P., Bustanji, S. Al, & Bereznychenko, V. (2026). Vehicle-wise validated ensemble learning framework for robust electric vehicle energy consumption prediction using physics-guided features and statistical benchmarking IN IN. Scientific Reports, 1(1), 1–61. https://doi.org/10.1038/s41598-026-55167-8
Pazhouh, S. M. M., Rastegar, N., Karami, M., Alizadehnia, S., & Amirmazlaghani, M. (2026). Fuel Consumption Prediction of Vehicles Using XGBoost Algorithm and ECU Performance Data in the New European Driving Cycle (NEDC). Conference: 14th International Conference on Internal Combustion Engines & OilAt: Tehran, 1–12. https://www.researchgate.net/publication/407128558
Shahariar, G. M. H., Bodisco, T. A., Surawski, N., Komol, M. M. R., Sajjad, M., Chu-Van, T., Ristovski, Z., & Brown, R. J. (2023). Real-driving CO2, NOx and fuel consumption estimation using machine learning approaches. Next Energy, 1(4), 100060. https://doi.org/10.1016/j.nxener.2023.100060
Shankar, G. (2026). A Comprehensive Survey on Fuel Consumption Optimization : Machine Learning , Energy Management , and Emerging Powertrain Technologies. IRE Journals, 9(11), 2963–2972. https://doi.org/10.64388/IREV9I11-1718015
Siswanto, D., Farid, A., Sulistiarini, E. B., & Pahlevi, R. P. (2025). Tinjauan Sistematis Parameter Kinerja Mesin Bensin Yang Diukur Melalui Engine Control Unit (ECU). The 8th Conference on Innovation and Application of Science and Technology (CIASTECH 2025) The, 8(1), 396–405. https://doi.org/10.31328/ciastech.v8i1.7498
Szumska, E. M., Pawlik, Ł., Frej, D., & Wilk-Jakubowski, J. Ł. (2025). Machine Learning Applications in Energy Consumption Forecasting and Management for Electric Vehicles: A Systematic Review. Energies, 18(20), 5420. https://doi.org/10.3390/en18205420
Topić, J., Škugor, B., & Deur, J. (2022). Neural Network-Based Prediction of Vehicle Fuel Consumption Based on Driving Cycle Data. In Sustainability (Vol. 14, Issue 2, p. 744). https://doi.org/10.3390/su14020744
Valladolid, J. D., & Ortiz, J. P. (2026). Energy Consumption Prediction for an Electric Vehicle Using Machine Learning: A Comparative Study of Regression, Ensemble, and LSTM-Based Models. Vehicles, 8(5), 1–9. https://doi.org/10.3390/vehicles8050099
Zahid, M. S., & Jamil, U. (2026). Data-driven machine learning techniques for fuel economy prediction in sustainable transportation systems. Green Energy and Intelligent Transportation, 5(1), 100303. https://doi.org/10.1016/j.geits.2025.100303
Zhang, X., Zhang, Z., Liu, Y., Xu, Z., & Qu, X. (2024). A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation. Renewable Energy, 234, 121243. https://doi.org/10.1016/j.renene.2024.121243
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