<![CDATA[Business development is in line with the development of increasingly sophisticated technology. This requires every company to compete and be motivated to increase its value as an indicator of success in managing the company so that investors are interested in investing. This study aims to design a K-means-based Decision Support System with a clustering approach to classify the growth rate of company value. Investment Opportunity Set (IOS) and profitability variables are the leading indicators of increasing company value. The problem formulation is how the design of this K-means-based decision support system can assist in classifying the growth rate of the company's value based on the IOS and profitability variables. This research aims to produce a decision support system that can organize the growth rate of company value using the K-means method. System testing is conducted to evaluate the effectiveness of the applied clustering method, focusing on the accuracy of the results. The weighting of IOS and profitability variables is based on the percentage of positive relationship to firm value, and the ultimate goal is to group companies with different growth rates. As a result, the K-means-based Decision Support System, or "Business Growth Prediction Decision Support System," successfully clustered the growth rate of firm value. With reasonable accuracy, measured using the silhouette coefficient, the calculation results show an overall mean silhouette coefficient of 0.684, close to the maximum value of 1. This result confirms that this decision support system can group companies in the L (Low), M (Medium), and H (High) categories based on the level of value growth, using the IOS and profitability variables as the leading indicators. Thus, this research supports decisions related to company growth strategies using K-means-based decision support systems.]]>
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