Random forest and decision tree algorithms for car price prediction - Putra, Purwa Hasan; Azanuddin, Azanuddin; Purba, Bister; Dalimunthe, Yulia Agustina (Lembaga Layanan Pendidikan Tinggi Wilayah I Sumatra Utara (LLDikti I), 2024)
At this time in the era of cars that use renewable energy fuels such as electric cars which are highly supported by the government so that it has an impact on used cars based on these problems an analysis is needed. Determining whether or not the price of buying or selling a used car is appropriate is one of the obstacles faced by the community in making decisions when buying or selling a car or vehicle. Therefore, most people choose an alternative by buying a used car that is still good and usable. One way to make price predictions is to use the Machine Learning method. In this study the authors used random forest and decision tree methods to predict car prices. The results of the research on car price prediction analysis using the random forest and decision tree methods have different percentage results. Where using the random forest method there is an accuracy: 72.13% whereas with the analysis of the decision tree method accuracy: 67.21%. So it can be concluded that the Random Forest method has better analytical accuracy than the Decision Tree method.