Used Car Price Prediction in Surabaya Using Random Forest Regressor Algorithms

Kinadi A. F., Andreswari R., Sutoyo E., Nugraha R., KAMIL A. A.

4th International Conference Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2022, Bandung, Indonesia, 23 - 24 November 2022 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/icadeis56544.2022.10037526
  • City: Bandung
  • Country: Indonesia
  • Keywords: Machine Learning, Mean Absolute Error, Random Forest Regressor, Root Mean Square Error
  • Istanbul Gelisim University Affiliated: Yes


The use of private vehicles during the Covid-19 pandemic has increased because private vehicles, especially cars, are considered as the safest mode of transportation to maintain distance and prevent transmission of the Covid-19 virus. Based on data from two different Indonesian secondary car market place, a comparison of a price sample of Car X in the city of Surabaya with the specifications for the 2015 to 2018 car years with car milage under 1000 kilometers, the used cars have a variety of prices hence a used car price prediction system is needed so that people can find out the average price of used cars sold in the market. In this study the author will use the Random Forest Regressor as a machine learning algorithm to predict the price of a used car with a dataset from the AtapData website. The reason for choosing the Random Forest Regressor is because the algorithm has the power to handle large amounts of data with high dimensions with categorical and numerical data types. The evaluation method used in this study is the Root Mean Absolute Error which produces a value of 0.55612 for validation data and 0.56638 for testing data, while the evaluation proceed with Mean Absolute Error produces a value of 0.45208 for validation data and 0.47576 for testing data.