Application of Data Mining to Predict Car Sales Using K-Means Clustering Method

Authors

  • Antonia Desya Universitas Bina Sarana Informatika Author
  • Dimas Ilham Pratama Universitas Bina Sarana Informatika Author
  • Muchammad Vico Airlangga Universitas Bina Sarana Informatika Author
  • Andi Diah Kuswanto Universitas Nusa Mandiri Author

DOI:

https://doi.org/10.62181/nb8vt960

Keywords:

Data Mining, Car Sales Prediction, K-Means Clustering Method

Abstract

The automotive industry is one of the key sectors in the economy. Accurate car sales prediction is crucial for automotive businesses to develop effective marketing and production strategies. Data mining, with its various techniques, offers solutions to assist automotive companies in predicting car sales. One popular data mining technique for predicting car sales is K-Means Clustering. This technique groups car sales data based on characteristics such as car model, price, sales region, and other factors. The clustering results can be used to identify sales patterns and trends, which can then be used to predict future sales. This paper discusses the application of K-Means Clustering for car sales prediction. It explains the steps involved in applying K-Means Clustering, its advantages and disadvantages, and provides an example of its application.

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Published

2024-05-31

How to Cite

Antonia Desya, Dimas Ilham Pratama, Muchammad Vico Airlangga, & Andi Diah Kuswanto. (2024). Application of Data Mining to Predict Car Sales Using K-Means Clustering Method. Journal of Economic Global, 1(3), 182-198. https://doi.org/10.62181/nb8vt960