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Simanjuntak, Robert Panca R.
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Computer Science & IT

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Improving car price prediction performance using stacking ensemble learning based on ann and random forest Tanga, Yulizchia Malica Pinkan; Simanjuntak, Robert Panca R.; Rofik, Rofik; Muslim, Much Aziz
Journal of Soft Computing Exploration Vol. 5 No. 3 (2024): September 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i3.462

Abstract

Determining the right selling price for a car can be a challenge for car sales companies. The selling price of a car is highly influenced by car characteristics such as brand, type, year of production, fuel type, and mileage. Therefore, the research aims to develop a more accurate model of car price prediction model by using a stacking ensemble technique that combines Random Forest and ANN. Random Forest is effective in handling outliers and reducing the risk of overfitting, while ANN has the advantage of capturing complex nonlinear patterns. The results show that the stacking ensemble model combining ANN and Random Forest can predict car sales prices by achieving an R2 value of 0.97. The results of this study can help distributors in selling cars make the right decisions regarding the sales price of cars. To improve the generalization of the model, future research is recommended to try a combination of different ensemble methods and the use of larger and more diverse datasets.
Co-Authors Much Aziz Muslim Rofik Rofik, Rofik Tanga, Yulizchia Malica Pinkan