Implementation of Data Mining in Determining Car Sales Strategy Using the K-Means Algorithm and Clustering K-Medoids
Implementasi Data Mining dalam Menentukan Strategi Penjualan Mobil Menggunakan Algoritma K-Means dan K-Medoids Clustering
DOI:
https://doi.org/10.21070/ups.8845Keywords:
Data Mining, K-Means, K-Medoids, SalesAbstract
The demand for transportation modes continues to increase, driving many people to own private vehicles to support their daily activities. Cars have become one of the main choices for the Indonesian people. This research aims to analyze car sales data using the K-Means and K-Medoids algorithms to identify transmission preferences and formulate sales strategies. The clustering results show that the K-Means algorithm is optimal at K=2 with a DBI of 0.492, dividing the data into low and high sales groups. K-Medoids provides the best results at K=5 with a DBI of 0.573, resulting in more detailed segmentation based on transmission dominance and sales volume. These findings indicate that K-Medoids is superior in generating specific strategic information. The results of this study are expected to serve as a reference in designing more adaptive car sales strategies to market changes, allowing manufacturers to better align product offerings with consumer needs and preferences.
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