Article Per Year (5 Year)
p-Index From 2021 - 2026
1.383
P-Index
This Author published in this journals
All Journal CESS (Journal of Computer Engineering, System and Science) Jurnal Sains dan Teknologi Jurnal Computer Science and Information Technology (CoSciTech) Journal Collabits
nungky, Nungky Awang Chandra
Universitas Mercu Buana
Computer Science & IT Economics, Econometrics & Finance Engineering Mathematics Physics Social Sciences

Published : 8 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : journal collabits

 1 
Comparative Analysis of Linear Regression and Random Forest for Used Car Price Prediction Syamsudi, Muhammad Faris Adjil; Daffa, Bimo Arya; Jarodi, Wisnu; Chandra, Nungky Awang
Journal Collabits Vol 3, No 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37646

Abstract

Manual estimation is often subjective and prone to human bias because the used car market has a complex pricing structure with non-linear depreciation. Objective: This study conducted a comparative analysis between Linear Regression and Random Forest algorithms to develop a more objective pricing model. Methods: The Kaggle dataset contains 5,000 entries indicating features such as manufacturer, model, engine size, and mileage for this study. The methodology included data cleaning, feature engineering, and outlier removal using the IQR method. For training and testing, the data was split 80:20. Results: "Year of Manufacture" was identified as the feature that most significantly influences price, and the evaluation results showed a significant difference in performance. Linear Regression achieved 82.33% accuracy, while Random Forest achieved 99.60% accuracy. Conclusion: Random Forest captures non-linear patterns and complex relationships in used car pricing better than Linear Regression, although it remains quite reliable for general trends.
Comparative Analysis of Linear Regression and Random Forest for Used Car Price Prediction Muhammad Faris Adjil Syamsudi; Bimo Arya Daffa; Wisnu Jarodi; Nungky Awang Chandra
Journal Collabits Vol. 3 No. 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37646

Abstract

Manual estimation is often subjective and prone to human bias because the used car market has a complex pricing structure with non-linear depreciation. Objective: This study conducted a comparative analysis between Linear Regression and Random Forest algorithms to develop a more objective pricing model. Methods: The Kaggle dataset contains 5,000 entries indicating features such as manufacturer, model, engine size, and mileage for this study. The methodology included data cleaning, feature engineering, and outlier removal using the IQR method. For training and testing, the data was split 80:20. Results: "Year of Manufacture" was identified as the feature that most significantly influences price, and the evaluation results showed a significant difference in performance. Linear Regression achieved 82.33% accuracy, while Random Forest achieved 99.60% accuracy. Conclusion: Random Forest captures non-linear patterns and complex relationships in used car pricing better than Linear Regression, although it remains quite reliable for general trends.
Co-Authors Alfin Hikmaturokhman Apif Arizki, Apif Arizki Bimo Arya Daffa Daffa, Bimo Arya firman, Firmansyah Budi Utomo Jarodi, Wisnu Mohamad Yusuf Muhammad Faris Adjil Syamsudi Roby Yasir Amri Shaffan, Ari Nur Syamsudi, Muhammad Faris Adjil Wisnu Jarodi