SAPUTRA, WAHYU EKA (2025) Comparative Performance Analysis of Random Forest, XGBoost, and LightGBM in Used Car Price Prediction Based on Training Speed and Accuracy. Tugas Akhir thesis, Informatics.
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Abstract
This study focuses on predicting used car prices, a domain influenced by complex variables such as vehicle condition, age, mileage, and other contributing factors. The primary issue addressed is the need for a predictive model that balances both accuracy and training efficiency, which is essential in addressing business challenges related to setting competitive and realistic prices. To this end, the research compares the performance of three leading machine learning algorithms—Random Forest, XGBoost, and LightGBM—by evaluating their training speed and predictive accuracy. The results reveal that each algorithm offers distinct strengths and limitations, providing valuable insights for selecting appropriate models based on practical needs in the automotive industry and predictive modelling development. Keywords: Used Car Price Prediction, Random Forest, XGBoost, LightGBM, Model Accuracy, Training Speed.
| Item Type: | Thesis (Skripsi, Tugas Akhir or Kerja Praktek) (Tugas Akhir) |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Fakultas Sains Dan Teknologi > S1 Informatika |
| Depositing User: | Kaprodi S1 Informatika UTY |
| Date Deposited: | 22 Jul 2025 03:48 |
| Last Modified: | 22 Jul 2025 03:48 |
| URI: | http://eprints.uty.ac.id/id/eprint/18325 |
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