A Hybrid AI Model Combining RF and SVM for Accurate Sales Prediction in SMEs to Support Digital Transformation - Purwanto, Eko; Prajadi Cipto Utomo, Bangun; Permatasari, Hanifah; Mohd, Farahwahida (Journal of Telecommunications and the Digital Economy, 2025)
Small and Medium Enterprises (SMEs) are increasingly adopting digital transformation to enhance their competitive advantage and operational efficiency. Accurate sales prediction is crucial for informed decision-making and business strategy optimisation. This research presents a Hybrid AI model, which combines the machine learning techniques of Random Forest (RF) and Support Vector Machine (SVM). Unlike traditional ensemble methods, this hybrid model leverages AI-driven decision-making processes to provide a more intelligent, adaptive, and accurate approach to sales forecasting in SMEs. By integrating RF's ensemble learning power with SVM's robust classification abilities, the model effectively addresses sales prediction challenges in dynamic business environments. The hybrid model was evaluated using key statistical metrics, achieving a Mean Squared Error (MSE) of 0.7254, Mean Absolute Error (MAE) of 0.4605, and an R-squared (R²) value of 0.99999, indicating outstanding predictive accuracy and a near-perfect fit to the data. These results demonstrate the model's capacity to capture complex sales data relationships, proving its practical applicability for sales forecasting. This study highlights the potential of hybrid AI models in advancing sales forecasting techniques, particularly in the context of digital transformation for SMEs.