Penerapan Algoritma K-Medoids untuk Pengelompokan Genre Game Berdasarkan Pola Penjualan - Zamzamil Amin; Satria Eka Pangestu; Muhammad Syafiq Alfaruq; Lusiana Efrizon; Rahmadenni, Rahmadenni (JURAL RISET RUMPUN ILMU TEKNIK, 2025)
The gaming industry continues to grow rapidly, with various genres having different sales patterns. This research aims to group the best game genres based on sales patterns using the K-Means Medoid algorithm. This method was chosen because of its ability to overcome data diversity and reduce the influence of outliers compared to the conventional K-Means algorithm. The data used in this research includes game sales information from various platforms, which is then analyzed to find distribution patterns and market trends. The results of this research show that certain game genres have superior sales patterns compared to others, and there are special characteristics in the groups that are formed. These findings are expected to help game developers and publishers in making strategic decisions regarding the development and marketing of their products. In addition, this research also contributes to the development of a data-based recommendation system that can be used to understand market preferences more accurately.