Game Popularity Level During Covid-19 Pandemic Using Agglomerative Hierarchical Clustering

Abstract

During the COVID-19 pandemic, various activities of people outside the home were disrupted and made people move more indoors. For some companies take advantage of this pandemic period as their advantage, especially digital game industry companies. Various games have been released and promoted, these games are published on various game platforms. Currently, Steam is one of the biggest gaming platforms. On this platform, there are a lot of games offered by game developers and provide game pages that are currently popular. However, the website does not provide the popularity level of the currently popular games. This causes ambiguity in determining which games have high, medium, or low popularity. This study tries to create a machine learning model to cluster these games into groups using Agglomerative Hierarchical Clusterin. The distance measure used is euclidean, cosine and manhattan/cityblock and uses single, average, complete and ward linkage. Based on the evaluation results, the best cluster results are the silhouette value of 0.639 and the calinski-harabasz value of 90.192.