<![CDATA[Rapid technological advances have affected various fields, especially in data management. The increasing volume of data generated from various sources demands efficient management and analysis methods. Data mining techniques offer a structured approach in processing, classifying, and grouping data to support decision making in various fields. This study is a systematic review of the application of data mining techniques, with a primary focus on the K-Means Clustering algorithm. This study analyzes the trend of data mining applications, especially in data classification and grouping to improve the effectiveness of decision making. Based on a systematic literature review, it was found that the K-Means Clustering algorithm is widely applied in sales analysis, market segmentation, stock optimization, and predictions in the social and health fields. In addition, other algorithms such as Decision Tree, Naïve Bayes, and K-Nearest Neighbor are also commonly used in predictive analysis and data classification. This study provides insight into the effectiveness of various data mining techniques and their future development opportunities.]]>
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