The Neural Network Based Multiple Linear Regression Model for Financial Profit Prediction

A Single Layer Perceptron Approach Integrated into a Financial Information System

Abstrak

Accurate financial forecasting plays a vital role in supporting business decision-making, particularly for organizations that depend on operational efficiency and strategic planning. CV. Surya Cipta Estetica Mandiri still manages its financial records manually, resulting in inaccurate estimations and limited predictive capabilities. This study proposes a profit prediction model using a Neural Network–based Multiple Linear Regression approach implemented through a Single Layer Perceptron (SLP) and integrated into a financial information system.

The dataset consists of 3,127 daily transaction records from January 2021 to April 2025, including date, category, total income, and total expense. The preprocessing phase includes handling missing values, label encoding, RobustScaler-based feature scaling, and an 80:20 train–test split. The model was developed using TensorFlow with a Sequential architecture, trained using 2,501 data points and evaluated using 626 data points.

The model demonstrates excellent predictive performance, achieving R² = 0.9988, MAPE = 1.03%, MAE = 258,080, RMSE = 893,381, and an overall accuracy of 98.97%. These results indicate that the SLP-based Multiple Linear Regression model provides stable and highly accurate profit predictions. Furthermore, the model can be expanded into a broader ERP system by integrating additional business modules such as finance, HR, and inventory management. Future research may incorporate external market variables, adaptive learning mechanisms, and non-linear algorithms such as Random Forest or Gradient Boosting to improve model robustness and predictive generalization.