Data Mining Analytics Application for Estimating Used Car Price during the Covid-19 Pandemic in Indonesia

Bramantiyo Eko Putro(1*), Dwi Indrawati(2),

(1) Universitas Suryakancana
(2) Universitas Suryakancana
(*) Corresponding Author
DOI: https://doi.org/10.23917/jiti.v21i2.18975

Abstract

Covid-19 has resulted in an increase in the people's need for vehicle ownership in order to avoid public transportation. People’s purchasing power, on the other hand, has also weakened. Therefore, they prefer to purchase affordable cars, such as used cars. Moreover, the Luxury Goods Sales Tax (PPnBM) discounts were officially applied to the purchase of the new cars in March 2021. This study aims at estimating the price of used cars using several data mining algorithms, such as Random Forest, K-Nearest Neighbour (KNN), and Naïve Bayes. By employing the RapidMiner tool, this study was able to evaluate the attributes affecting car prices. From the experimental results, random forest producers have the highest accuracy of 95.46%.  Then, this study figured out that brand, engine capacity, kilometres, colours, years, number of passengers, and transmissions are the most influential attributes to determine the estimation of the used car prices.

Keywords

data mining; estimation; used cars; random forest; k-nearest neighbour; naïve bayes

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References

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