<![CDATA[In the era of video game industry development, optimizing monetization within games has become a primary concern for developers. Analyzing revenue data from banner characters and categorizing characters are relevant steps, especially for players who are new to the game. This involves providing character recommendations based on the income generated from game banners. The main issue faced by players is the difficulty in choosing the right banner characters to invest in, considering that each banner features different characters with varying appearance statistics. Additionally, players need guidance in identifying characters with the most potential to enhance their performance. The approach employed in this research involves using Clustering Algorithms to group characters based on factors relevant to banner income, including character popularity, availability in banners, and their unique abilities. Detailed transaction data analysis in the game is conducted using K-Means Clustering and Hierarchical Clustering algorithms to generate character groups. The research results demonstrate that employing clustering algorithms produces character groups that provide insights for players to consider allocating their resources to characters categorized in high-income or low-income groups. By using character recommendations based on these groups, players can be guided to select characters that align with their preferences and gameplay styles.]]>
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