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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) REKAYASA JIKO (Jurnal Informatika dan Komputer) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI INTI Nusa Mandiri Jurnal Abdimas Perbanas JUSIFOR : Jurnal Sistem Informasi dan Informatika Jurnal Pengabdian Ibnu Sina Jurnal Pengabdian Masyarakat Bangsa
Hasanah, Riyan Latifahul
PPPM STMIK Nusa Mandiri
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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OPTIMALISASI PREDIKSI JARAK TEMPUH KENDARAAN LISTRIK MENGGUNAKAN XGBOOST DAN FEATURE SELECTION RANDOM FOREST Saelan, M. Rangga Ramadhan; Hasanah, Riyan Latifahul; Fauziah, Siti
INTI Nusa Mandiri Vol. 20 No. 2 (2026): INTI Periode Februari 2026
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/zahvvq98

Abstract

Electric vehicle (EV) adoption is increasing with increasing awareness of clean energy and environmental concerns. However, range anxiety, the uncertainty in estimating driving range, remains a major barrier. This study aims to develop a predictive model for EV range based on technical specifications to provide more accurate estimates and alleviate user concerns. A Machine Learning approach is applied, using Random Forest for feature selection and XGBoost as the primary prediction algorithm. The dataset consists of 478 EV records with 22 attributes, including battery capacity, efficiency, dimensions, and speed. Key features affecting range prediction include battery_capacity_kWh, efficiency_wh_per_km, and height_mm. The XGBoost model demonstrates strong predictive performance with an R² of 0.978, an MAE of 10.555, and an RMSE of 15.180. These results suggest that combining Random Forest and XGBoost offers a promising solution to improve the accuracy of EV range estimation, potentially reducing range anxiety and supporting wider EV adoption
Co-Authors Anasanti, Mila Desi ANI OKTARINI SARI, ANI OKTARINI Antonius Yadi Kuntoro Daniati Uki Eka Saputri Djamal, Farianti Dwiza Riana Ferda Ernawan M. Rangga Ramadhan Saelan Na'am, Jufriadif Pardede, Hilman Ferdinandus Pratiwi, Risca Lusiana Semestanti, Prisilia Setiaji Setiaji, Setiaji Siswandi, Agus Siti Fauziah Tati Mardiana, Tati