<![CDATA[Financial management and forecasting are critical aspects in supporting decision-making within an organization, particularly amid the increasing demand for fast and accurate data analysis. In general, many companies in Indonesia still face challenges in utilizing historical financial data to optimally predict revenue. This issue is also encountered by a company that continues to rely on manual record-keeping using spreadsheet-based systems, which makes it difficult to conduct analysis and forecast future financial conditions. This study aims to implement a linear regression method to predict revenue based on historical financial transaction data. The methodology employed follows the CRISP-ML(Q) framework, which includes business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The modeling process is carried out by developing a linear regression model using independent and dependent variables. The results indicate that the constructed linear regression model is capable of generating revenue predictions with a relatively low error rate, thereby effectively representing patterns within the historical data. Model evaluation using error metrics demonstrates that the model performs adequately within the context of the dataset used. In conclusion, the linear regression method is effective for revenue prediction and can support data-driven decision-making processes. Future research is recommended to enhance the model by incorporating more complex variables and applying alternative prediction methods to improve accuracy.]]>
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