Application of Data Mining For Clustering Car Sales Using The K-Means Clustering Algorithm - Hutasoit, Michael Nico; Fa’rifah, Riska Yanu; Andreswari, Rachmadita (Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa, 2023)
In the digital era, data is at the core of business continuity. The need for fast, precise and accurate information is needed. Cars are one of the tertiary needs. This means of transportation is a relatively fast development and innovation business. Car sales in Indonesia recorded a reasonably high number in 2014 - 2018, namely 4.157.580 units sold. The highest sales were MPV car types being the most popular type of car, and there are many types of cars in Indonesia, including Sedans, SUV, 7 Seater SUV, and City Car types, and the enthusiasts need to play more. Hence, it is exciting to study. The variety of car brands with competing prices makes it difficult for consumers to choose the right car to buy according to their needs. This can be solved by applying data mining to cluster car sales using the k-means clustering algorithm. The goal is to know the characteristics of the car from each attribute. The k-Means algorithm is used for cluster formation based on five attributes: CC, Tank Capacity, GVW (Kg), Seater, and Door. The elbow and silhouette score methods determine the optimal number of clusters (k). The result is 4 clusters, cluster 0 (High-Performance Heavy Car), cluster 1 (Small Family Car), cluster 2 (High-Performance Small Car), and cluster 3 (Medium Performance Car). The 4 Cluster results are based on the evaluation/validation of the Elbow Method and Silhouette.