Analysis of Consumer Preferences for Used Cars Using K-Means Clustering as a Basis for Building Business Actions

Transportation is a crucial aspect of modern mobility, enabling the movement of goods and individuals. Private vehicles, such as motorcycles and cars, are the dominant choice for many due to their convenience and practicality. However, with numerous car options, consumers often struggle to select used cars that match their needs and preferences. In this research, data mining is employed to cluster used cars based on consumer preferences for price, rating, and performance using the K-Means Clustering algorithm. The data consists of used car information from various sources. The K-Means algorithm groups the cars into clusters based on price, rating, and performance criteria. The results reveal three consumer groups with different preferences in these areas. This information provides valuable insights for consumers to make informed decisions and offers guidance to the automotive industry for designing more effective business actions.