<![CDATA[The rapid development of information technology has increased the utilization of sales data as a basis for business decision-making, including in the smartphone industry, which is characterized by intense brand competition. The availability of large-scale sales data requires analytical methods that are capable of processing data systematically in order to generate valuable information. This study aims to analyze smartphone sales patterns based on product attributes and to evaluate the performance of classification methods in grouping sales data effectively. The method used in this research is data mining with the Decision Tree algorithm implemented using RapidMiner software. The dataset employed is secondary smartphone sales data that undergo several stages, including data preprocessing, attribute selection, model training, and evaluation using accuracy, precision, recall, and confusion matrix metrics. The results indicate that user rating and storage capacity attributes have a dominant influence in the classification process. The Decision Tree model is able to classify data into several classes with an accuracy of 91.45% and a micro-average value of 91.46%, showing the best performance in the dominant class. This study concludes that the Decision Tree algorithm demonstrates good classification performance, is easy to interpret, and can be utilized as a data-driven analytical tool to support decision-making and marketing strategy development in the smartphone industry.]]>
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