<![CDATA[This research topic discusses the comparison of the K-Means and C4.5 algorithms in the application of data mining to predict aquarium sales in a company. K-Means is a clustering algorithm that functions to group data based on similarity, for example grouping customers based on frequency or type of purchase. This helps companies understand market segments and design marketing strategies accordingly. Meanwhile, C4.5 is a classification algorithm that builds decision trees based on important attributes that influence sales, such as price, season, or promotions. This algorithm is able to predict sales categories, such as increases or decreases, based on historical data. By comparing these two algorithms, the research sought to find out which algorithm is more effective in helping companies predict sales and make strategic decisions. A combination of the two can also be used, with K-Means grouping the data first, then C4.5 classifying each segment formed. These results can provide more accurate sales predictions and more effective marketing strategies. This research is important to understand the effectiveness of algorithms in data mining to improve business decision making.]]>
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