Pemanfaatan Algoritma K-Means Clustering Pada Sistem Rental Mobil
DOI:
https://doi.org/10.47065/jimat.v5i3.391Keywords:
Data Mining; Clustering; K-Means; Car Rental; RapidminerAbstract
PT. Station Armada Indonesia is one of the companies engaged in the car rental service sector. With the many types of car choices offered, it is not uncommon for many customers to feel confused in choosing what type of car suits their needs. This problem is often experienced by customers who are confused by the many choices of car types available. In this study, the k-means algorithm was used to group cars based on several attributes. The k-means algorithm can be used to group car type data to help provide recommendations for choosing a car type. The purpose of this study is to make it easier for customers to choose the type of car that is most in demand and as material for PT. Station Armada Indonesia to respond better to market changes and achieve better results. Grouping car rental fleets based on rental prices and mileage by utilizing the k-means algorithm can help PT. Station Armada Indonesia group car types. From the grouping results, two cluster groups were obtained with the character of the first cluster being less in demand by customers and the second cluster group being the most in demand by customers. So that the company can easily prepare the type of fleet that is most in demand. In the application of data mining methods using k-means is very helpful and makes it easier for PT. Station Armada Indonesia to develop more effective marketing and offering strategies. By grouping car types with the implementation of k-means can facilitate customer knowledge in choosing car types based on customer needs.
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