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All Journal Jurnal Nasional Komputasi dan Teknologi Informasi Jambura Journal of Food Technology JSAI (Journal Scientific and Applied Informatics) JAGO TOLIS : Jurnal Agrokompleks Tolis Jurnal Abdimas Terapan Jurnal Ilmu Komputer dan Sistem Informasi KETIK : Jurnal Informatika Journal Of Software Engineering And Communication Journal Of Fisheries Agribusiness J-CEKI PESHUM KREATIF: Jurnal Pengabdian Masyarakat Nusantara
Fuad Pontoiyo
Universitas Negeri Gorontalo
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Law, Crime, Criminology & Criminal Justice Library & Information Science Neuroscience Physics Social Sciences Transportation Other

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Journal : JSAI (Journal Scientific and Applied Informatics)

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Analisis Regresi Linear dan Ensemble Learning Berbasis Kontribusi Fitur dalam Prediksi Harga Mobil Listrik Idris, Nur Oktavin; Pontoiyo, Fuad
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9891

Abstract

This study aims to analyze the performance of linear regression and ensemble learning methods in predicting electric vehicle prices based on technical specifications, as well as to examine the contribution of key features to the prediction results. The main challenge in electric vehicle price prediction lies in the high price variability driven by nonlinear relationships among technical attributes, which are difficult to capture using simple linear models. Linear regression was employed as a baseline model, while Random Forest and Gradient Boosting were used as ensemble learning approaches. The dataset was obtained from Kaggle and processed through data cleaning, categorical encoding, normalization, and an 80:20 train–test split. Model performance was evaluated using mean squared error (MSE) and the coefficient of determination (R²). The results indicate that the Gradient Boosting model achieved the best performance, with an MSE of 8.63 and an R² of 0.891, outperforming both Random Forest and linear regression models. Feature contribution analysis reveals that vehicle acceleration time is the most influential factor in determining electric vehicle prices. These findings demonstrate that ensemble learning not only improves predictive accuracy but also provides analytical insights into the key technical factors shaping electric vehicle pricing.
Co-Authors Abdul Haris PanaI Ahmad Faqih Ahmad Faqih Ahmad Faqih Ardiyanto Saleh Modjo Dewi Wahyuni K. Baderan Farid Farid Hasim Hasim Hasim Hasim Hasim, H Iswan Dunggio La Alio La Alio Liputo, Burhan Mahludin H. Baruwadi Marini Susanti Hamidun Muh. Arfah Syam Mukhtar, Sigit Adi Nur Oktavin Idris Nur Oktavin Idris Ramli Utina Rosbin Pakaya Sukirman Rahim Syam, Muh Arfah Syam, Muh. Arfah Yunita Djamalu