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All Journal Jurnal Informatika Upgris Jurnal Nasional Komputasi dan Teknologi Informasi JSAI (Journal Scientific and Applied Informatics) Jurnal Manajemen Informatika dan Sistem Informasi Jurnal Sistem Komputer dan Informatika (JSON) Dikmas: Jurnal Pendidikan Masyarakat dan Pengabdian Jurnal Ilmu Komputer dan Sistem Informasi Journal Of Software Engineering And Communication
Nur Oktavin Idris
Universitas Negeri Gorontalo
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Neuroscience Transportation

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Evaluasi Model Machine Learning untuk Prediksi Harga Mobil dengan Perbandingan Ensemble dan Regresi Linear Nur Oktavin Idris; Fuad Pontoiyo
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 4 No. 1 (2025): Januari 2025
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v4i1.181

Abstract

Car price prediction is a major challenge in the automotive industry because it is influenced by various factors, such as technical specifications, fuel type, and transmission system. This research aims to evaluate and compare the performance of linear regression models and ensemble learning methods, namely Random Forest and Gradient Boosting, in predicting car prices. The dataset used comes from Kaggle, with 11,914 rows of data and 16 features. The research process includes the stages of data understanding, data preparation, modeling, and evaluation using the Mean Squared Error (MSE) and R-squared (R²) metrics. The research results show that the Gradient Boosting model has the best performance, with an R² value of 0.963868 and the lowest MSE compared to other models, followed by Random Forest with an R² of 0.899657. In contrast, linear regression showed lower performance, with an R² of 0.417905, indicating its limitations in handling non-linear relationships in the data. The prediction results from the best model show price estimates that are quite close to actual prices, although some improvements still need to be made through hyperparameter optimization. This research confirms that ensemble learning methods, especially Gradient Boosting, provide a more effective approach to predicting car prices than linear regression. This model has the potential to be applied in the automotive industry to improve the accuracy of vehicle price estimates for manufacturers, dealers, and consumers.
Analisis Regresi Linear dan Ensemble Learning Berbasis Kontribusi Fitur dalam Prediksi Harga Mobil Listrik Nur Oktavin Idris; Fuad Pontoiyo
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 Adam, Novita Andi Mulawati Mas Pratama Badaruddin, Muliati Farid Farid Farid Farid, F. Fuad Pontoiyo Maryati Kadir Thalib Nurjana Suleman Rapika Anwar Satriadi D. Ali Sri Oktavia Dai Suleman, Fitriyanti Suleman, Nurjana Utiarahman, Siti Andini Wiwik Handayani Zufrianto K Dunggio