<![CDATA[ This study proposes an error mitigation model applied to the financial sector in higher education, aiming to improve the prediction accuracy in a linear regression model used to monitor and manage campus finances. By analyzing the error distribution of the original model, an additional model is developed to reduce the impact of errors on identified sensitive areas. These two models are then combined into one ensemble model, which is able to reduce the standard residual error (RSE) by up to 7%. The use of this ensemble model has proven effective in improving the accuracy of the results compared to a single model. A case study using university financial data, including parameters such as operating costs, revenues, and budget allocations, shows that error mitigation can provide significant improvements in campus financial management, especially in terms of budget planning and expenditure prediction. This study opens up opportunities for wider application in the higher education sector that requires more accurate and efficient financial management]]>
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