A Hybrid AI Model Combining RF and SVM for Accurate Sales Prediction in SMEs to Support Digital Transformation
Main Article Content
Keywords
Hybrid Artificial Intelligence, Random Forest, Support Vector Machine, Sales Prediction, Digital Transformation
Abstract
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.
References
AbdElminaam, D. S., Mohamed, M., Khaled, S., Hany, F., Magdy, M., & Sherif, Y. (2024). Leveraging Machine Learning for Accurate Store Sales Prediction: A Comparative Study. 2024 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), pp. 355–362 https://doi.org.10.1109/MIUCC62295.2024.10783509
Ahaggach, H., Abrouk, L., & Lebon, É. (2024). Systematic Mapping Study of Sales Forecasting: Methods, Trends, and Future Directions. Forecasting, 6(3), 502–532. https://doi.org/10.3390/forecast6030028
Anchuri, S. (2024). Machine Learning-Driven Demand Forecasting: A Comparative Analysis of Advanced Techniques and Real-Time Integration. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 1352–1361. https://doi.org/10.32628/cseit241061175
Boström, H. (2024). Example-Based Explanations of Random Forest Predictions. In Miliou, I., Piatkowski, N., & Papapetrou, P. (eds), Advances in Intelligent Data Analysis XXII. IDA 2024. Lecture Notes in Computer Science, vol. 14642. Springer, Cham (pp. 185–196). Springer Science+Business Media. https://doi.org/10.1007/978-3-031-58553-1_15
Chen, S. (2023). Breast Cancer Prediction Based on RF-SVM. Applied and Computational Engineering, 8(1), 657–666. https://doi.org/10.54254/2755-2721/8/20230293
Chin, F. Y., & Goh, Y. K. (2024). Enhancing classification in high-dimensional data with robust rMI-SVM feature selection. Bulletin of Electrical Engineering and Informatics, 13(5), 3644–3652. https://doi.org/10.11591/eei.v13i5.7938
Dudek, G. (2022). A Comprehensive Study of Random Forest for Short-Term Load Forecasting. Energies, 15(20), 7547. https://doi.org/10.3390/en15207547
Gaertner, T., Lippert, C., & Konigorski, S. (2024). Automated Demand Forecasting in small to medium-sized enterprises. arXiv. https://doi.org/10.48550/arxiv.2412.20420
Ganesan, P. (2024). AI-Powered Sales Forecasting: Transforming Accuracy and Efficiency in Predictive Analytics. Journal of Artificial Intelligence, Machine Learning and Data Science, 2(1), 1213–1216. https://doi.org/10.51219/jaimld/premkumar-ganesan/280
Ganguly, P., & Mukherjee, I. (2024). Enhancing Retail Sales Forecasting with Optimized Machine Learning Models. arXiv. https://doi.org/10.48550/arxiv.2410.13773
Guo, J., Li, W., & Hu, J. (2024). Robust Support Vector Machine Based on Sample Screening. 2024 14th International Conference on Information Science and Technology (ICIST), Chengdu, China, 2024, pp. 539–546. https://doi.org/10.1109/icist63249.2024.10805427
Gupta, A., & Agarwal, P. (2024). Enhancing Sales Forecasting Accuracy through Integrated Enterprise Resource Planning and Customer Relationship Management using Artificial Intelligence. 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT), Vellore, India, 2024, pp. 1–6. https://doi.org/10.1109/aiiot58432.2024.10574785
Gupta, R., & Singla, P. (2023). Prediction of AQI using hybrid approach in machine learning. ICTACT Journal on Soft Computing, 13(3), 2917–2921. https://doi.org/10.21917/ijsc.2023.0412
Ignatenko, V., Surkov, A., & Koltcov, S. (2024). Random forests with parametric entropy-based information gains for classification and regression problems. PeerJ, 10. https://doi.org/10.7717/peerj-cs.1775
Ingole, B. S., Ramineni, V., Jayaram, V., Banarse, A. R., Krishnappa, M. S., Pulipeta, N. K., Parlapalli, V., & Pandy, G. (2024). Prediction and Early Detection of Heart Disease: A Hybrid Neural Network and SVM Approach. 2024 IEEE 17th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC). pp. 282–286. https://doi.org/10.1109/mcsoc64144.2024.00054
Islam, M. D. T., Ayon, E. H., Ghosh, B. P., Chowdhury, M. D., Shahid, R., Roy puja, A., Rahman, S., Bhuiyan, M. S., & Nguyen, T. N. (2024). Revolutionizing Retail: A Hybrid Machine Learning Approach for Precision Demand Forecasting and Strategic Decision-Making in Global Commerce. Journal of Computer Science and Technology Studies, 6(1), 33–39. https://doi.org/10.32996/jcsts.2024.6.1.4
Jothiraj, S., Chellam, S. I., Rajeshwari, V., & Sri, C. K. (2024). A Comprehensive Analysis of Predicting Future Sale and Forecasting Using Random Forest Regression. In D. Satishkumar & M. Sivaraja (Eds), Industry Applications of Thrust Manufacturing: Convergence with Real-Time Data and AI (pp. 177–196). IGI Global. https://doi.org/10.4018/979-8-3693-4276-3.ch007
Kholiev, V., & Barkovska, O. (2023). Analysis of the of training and test data distribution for audio series classification. Ìnformacìjno-Keruûčì Sistemi Na Zalìzničnomu Transportì, 28(1), 38–43. https://doi.org/10.18664/ikszt.v28i1.276343
Kumar, L., Shukla, S., & Wadhwani, R. (2024). Analysis Of Feature Noise On Standard SVM With Linear Kernel. 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS), 1–6. https://doi.org/10.1109/sceecs61402.2024.10482097
Kyrsanov, O. O., & Krivenko, S. (2024). Feature engineering for the implementation of machine learning in clinical data processing. Ìnfokomunìkacìjnì Tehnologìï Ta Elektronna Ìnženerìâ, 4(2), 162–171. https://doi.org/10.23939/ictee2024.02.162
Lee, G. Y., Alzamil, L., Doskenov, B., & Termehchy, A. (2021). A Survey on Data Cleaning Methods for Improved Machine Learning Model Performance. ArXiv. https://dblp.uni-trier.de/db/journals/corr/corr2109.html#abs-2109-07127
Magesh, T., Supriya, S., Yuvaprakash, A., Vishvapriya, R. T., Nisha, C. S., & Govindaraajan, P. (2024). Hybrid Weather Forecasting: Integrating LSTM Neural Networks and Random Forest Models for Enhanced Accuracy. 2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI), pp. 1–5 https://doi.org/10.1109/raeeucci61380.2024.10547997
Maindola, M., Al–Fatlawy, R. R., Kumar, R., Boob, N. S., Sreeja, S. P., Sirisha, N., & Srivastava, A. (2024). Utilizing Random Forests for High-Accuracy Classification in Medical Diagnostics. 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), pp. 1679–1685. https://doi.org/10.1109/ic3i61595.2024.10828609
Miao, Y., & Xu, Y. (2024). Random Forest-Based Analysis of Variability in Feature Impacts. 2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA), pp. 1130–1135. https://doi.org/10.1109/icipca61593.2024.10708791
Mittal, M., Al–Jawahry, H. M., Varshney, N., Kumar, S. P., Michaelson, J. J., & Reddy, R. (2024). Improving Support Vector Machine Performance with Advanced Kernel Methods. 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), pp. 1749–1754. https://doi.org/10.1109/ic3i61595.2024.10828664
Mohd, N., Kumar, I., & Agrawal, A. J. (2023). Machine Learning Applications and the use of Big Data to Manage the Operation of SMEs. 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI), pp. 664–668. https://doi.org/10.1109/iccsai59793.2023.10421701
Mushagalusa, C. A., Fandohan, A. B., & Kakaï, R. G. (2022). Random Forests in Count Data Modelling: An Analysis of the Influence of Data Features and Overdispersion on Regression Performance. Journal of Probability and Statistics, 2022, 1–21. https://doi.org/10.1155/2022/2833537
Mustaqeem, M., & Siddiqui, T. (2023). A Hybrid Software Defects Prediction Model for Imbalance Datasets Using Machine Learning Techniques: (S-SVM Model). Journal of Autonomous Intelligence, 6(1), 559. https://doi.org/10.32629/jai.v6i1.559
Ocran, G., Yusuf, S. O., Owusu, P., Boateng, E. A., Krampah, S. O., & Paul-Adeleye, A. H. (2024). AI-driven business analytics for SMES: Unlocking value through predictive and prescriptive analytic. International Journal of Science and Research Archive, 13(1), 3009–3022. https://doi.org/10.30574/ijsra.2024.13.1.2001
Ojukwu, P. U., Omokhoa, H. E., Odionu, C. S., Azubuike, C., & Sule, A. K. (2025). Digital Transformation and Optimization Framework for Advancing SME Growth and Operational Effectiveness. International Journal of Research and Innovation in Social Science, VIII(XII), 4607–4628. https://doi.org/10.47772/ijriss.2024.8120387
Orra, A., Sahoo, K., Choudhary, H. (2023). Machine Learning-Based Hybrid Models for Trend Forecasting in Financial Instruments. In Thakur, M., Agnihotri, S., Rajpurohit, B. S., Pant, M., Deep, K., & Nagar, A. K. (eds). Soft Computing for Problem Solving. Lecture Notes in Networks and Systems, vol. 547 (pp. 337–353). Springer, Singapore. https://doi.org/10.1007/978-981-19-6525-8_26
Pai, K. R. M., Batool, M., Ravi, N., Ramteke, S. P., Shree, A. N. R., & Meenakshi, S. (2024). Enhancing Sales Forecasting and Prediction with Cutting-Edge Machine Learning Methods. 2024 Second International Conference on Data Science and Information System (ICDSIS), pp. 1–6 https://doi.org/10.1109/icdsis61070.2024.10594627
Pandey, V., Kumar, A., Gupta, S. P., Kumar, S., & Tyagi, P. (2024). Navigating Digitalization: AHP Insights for SMEs’ Strategic Transformation. International Journal of Innovative Science and Research Technology, 9(4), 693–703 https://doi.org/10.38124/ijisrt/ijisrt24apr767
Plevris, V., Solorzano, G., Bakas, N., & Ben Seghier, M. (2022). Investigation of performance metrics in regression analysis and machine learning-based prediction models. 8th European Congress on Computational Methods in Applied Sciences and Engineering. https://doi.org/10.23967/eccomas.2022.155
Shaikh, Z. P. (2024). Leveraging Artificial Intelligence for Digital Actionable Transformation of Business. In H. Taherdoost, G. Drazenovic, M. Madanchian, I. Khan, & O. Arshi (Eds), Business Transformation in the Era of Digital Disruption (pp. 181–212). IGI Global. https://doi.org/10.4018/979-8-3693-7056-8.ch007
Sharma, H., Kumar, D., Baskar, S., Shukla, D., Singh, D., Tiwari, S., & Danish, M. (2024). Enhancement of Sales Forecasting and Prediction with Machine Learning Methods. International Research Journal of Computer Science, 11(11), 641–644. https://doi.org/10.26562/irjcs.2024.v1111.02
Shukla, M. (2024). The Impact of AI on Improving the Efficiency and Accuracy of Managerial Decisions. International Journal For Science Technology And Engineering, 12(7), 830–842. https://doi.org/10.22214/ijraset.2024.63652
Sun, Y., & Li, T. (2024). A transformer-based framework for enterprise sales forecasting. PeerJ, 10, e2503. https://doi.org/10.7717/peerj-cs.2503
Tao, S., Fan, Y., & Zhang, R. (2020). A Hybrid Forecasting Model for Stock Price Prediction. CSAE ’20: Proceedings of the 4th International Conference on Computer Science and Application Engineering, pp. 1–5. https://doi.org/10.1145/3424978.3424985
Tran, C. T. (2023). Ensemble learning approaches for classification with high-dimensional data. Journal of Science and Technique, 12(01). https://doi.org/10.56651/lqdtu.jst.v12.n1.659.ict
Wong, K. K. L. (2023). Support Vector Machine. Ch. 8 (pp. 149–176) in Cybernetical Intelligence: Engineering Cybernetics with Machine Intelligence, IEEE. https://doi.org/10.1002/9781394217519.ch8
Zhao, J., Lee, C.-D., Chen, G., & Zhang, J. (2024). Research on the Prediction Application of Multiple Classification Datasets Based on Random Forest Model. 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS), pp. 156–161. https://doi.org/10.1109/icpics62053.2024.10795875
Zheng, S., & Ding, C. (2018). Minimal Support Vector Machine. ArXiv. https://dblp.uni-trier.de/db/journals/corr/corr1804.html#abs-1804-02370
Zhu, A. (2025). VMD-SVM-based Retail Product Demand Forecasting Model. Scientific Journal of Economics and Management Research, 7(2), 24–31. https://doi.org/10.54691/g7pfr887
Ahaggach, H., Abrouk, L., & Lebon, É. (2024). Systematic Mapping Study of Sales Forecasting: Methods, Trends, and Future Directions. Forecasting, 6(3), 502–532. https://doi.org/10.3390/forecast6030028
Anchuri, S. (2024). Machine Learning-Driven Demand Forecasting: A Comparative Analysis of Advanced Techniques and Real-Time Integration. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 1352–1361. https://doi.org/10.32628/cseit241061175
Boström, H. (2024). Example-Based Explanations of Random Forest Predictions. In Miliou, I., Piatkowski, N., & Papapetrou, P. (eds), Advances in Intelligent Data Analysis XXII. IDA 2024. Lecture Notes in Computer Science, vol. 14642. Springer, Cham (pp. 185–196). Springer Science+Business Media. https://doi.org/10.1007/978-3-031-58553-1_15
Chen, S. (2023). Breast Cancer Prediction Based on RF-SVM. Applied and Computational Engineering, 8(1), 657–666. https://doi.org/10.54254/2755-2721/8/20230293
Chin, F. Y., & Goh, Y. K. (2024). Enhancing classification in high-dimensional data with robust rMI-SVM feature selection. Bulletin of Electrical Engineering and Informatics, 13(5), 3644–3652. https://doi.org/10.11591/eei.v13i5.7938
Dudek, G. (2022). A Comprehensive Study of Random Forest for Short-Term Load Forecasting. Energies, 15(20), 7547. https://doi.org/10.3390/en15207547
Gaertner, T., Lippert, C., & Konigorski, S. (2024). Automated Demand Forecasting in small to medium-sized enterprises. arXiv. https://doi.org/10.48550/arxiv.2412.20420
Ganesan, P. (2024). AI-Powered Sales Forecasting: Transforming Accuracy and Efficiency in Predictive Analytics. Journal of Artificial Intelligence, Machine Learning and Data Science, 2(1), 1213–1216. https://doi.org/10.51219/jaimld/premkumar-ganesan/280
Ganguly, P., & Mukherjee, I. (2024). Enhancing Retail Sales Forecasting with Optimized Machine Learning Models. arXiv. https://doi.org/10.48550/arxiv.2410.13773
Guo, J., Li, W., & Hu, J. (2024). Robust Support Vector Machine Based on Sample Screening. 2024 14th International Conference on Information Science and Technology (ICIST), Chengdu, China, 2024, pp. 539–546. https://doi.org/10.1109/icist63249.2024.10805427
Gupta, A., & Agarwal, P. (2024). Enhancing Sales Forecasting Accuracy through Integrated Enterprise Resource Planning and Customer Relationship Management using Artificial Intelligence. 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT), Vellore, India, 2024, pp. 1–6. https://doi.org/10.1109/aiiot58432.2024.10574785
Gupta, R., & Singla, P. (2023). Prediction of AQI using hybrid approach in machine learning. ICTACT Journal on Soft Computing, 13(3), 2917–2921. https://doi.org/10.21917/ijsc.2023.0412
Ignatenko, V., Surkov, A., & Koltcov, S. (2024). Random forests with parametric entropy-based information gains for classification and regression problems. PeerJ, 10. https://doi.org/10.7717/peerj-cs.1775
Ingole, B. S., Ramineni, V., Jayaram, V., Banarse, A. R., Krishnappa, M. S., Pulipeta, N. K., Parlapalli, V., & Pandy, G. (2024). Prediction and Early Detection of Heart Disease: A Hybrid Neural Network and SVM Approach. 2024 IEEE 17th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC). pp. 282–286. https://doi.org/10.1109/mcsoc64144.2024.00054
Islam, M. D. T., Ayon, E. H., Ghosh, B. P., Chowdhury, M. D., Shahid, R., Roy puja, A., Rahman, S., Bhuiyan, M. S., & Nguyen, T. N. (2024). Revolutionizing Retail: A Hybrid Machine Learning Approach for Precision Demand Forecasting and Strategic Decision-Making in Global Commerce. Journal of Computer Science and Technology Studies, 6(1), 33–39. https://doi.org/10.32996/jcsts.2024.6.1.4
Jothiraj, S., Chellam, S. I., Rajeshwari, V., & Sri, C. K. (2024). A Comprehensive Analysis of Predicting Future Sale and Forecasting Using Random Forest Regression. In D. Satishkumar & M. Sivaraja (Eds), Industry Applications of Thrust Manufacturing: Convergence with Real-Time Data and AI (pp. 177–196). IGI Global. https://doi.org/10.4018/979-8-3693-4276-3.ch007
Kholiev, V., & Barkovska, O. (2023). Analysis of the of training and test data distribution for audio series classification. Ìnformacìjno-Keruûčì Sistemi Na Zalìzničnomu Transportì, 28(1), 38–43. https://doi.org/10.18664/ikszt.v28i1.276343
Kumar, L., Shukla, S., & Wadhwani, R. (2024). Analysis Of Feature Noise On Standard SVM With Linear Kernel. 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS), 1–6. https://doi.org/10.1109/sceecs61402.2024.10482097
Kyrsanov, O. O., & Krivenko, S. (2024). Feature engineering for the implementation of machine learning in clinical data processing. Ìnfokomunìkacìjnì Tehnologìï Ta Elektronna Ìnženerìâ, 4(2), 162–171. https://doi.org/10.23939/ictee2024.02.162
Lee, G. Y., Alzamil, L., Doskenov, B., & Termehchy, A. (2021). A Survey on Data Cleaning Methods for Improved Machine Learning Model Performance. ArXiv. https://dblp.uni-trier.de/db/journals/corr/corr2109.html#abs-2109-07127
Magesh, T., Supriya, S., Yuvaprakash, A., Vishvapriya, R. T., Nisha, C. S., & Govindaraajan, P. (2024). Hybrid Weather Forecasting: Integrating LSTM Neural Networks and Random Forest Models for Enhanced Accuracy. 2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence (RAEEUCCI), pp. 1–5 https://doi.org/10.1109/raeeucci61380.2024.10547997
Maindola, M., Al–Fatlawy, R. R., Kumar, R., Boob, N. S., Sreeja, S. P., Sirisha, N., & Srivastava, A. (2024). Utilizing Random Forests for High-Accuracy Classification in Medical Diagnostics. 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), pp. 1679–1685. https://doi.org/10.1109/ic3i61595.2024.10828609
Miao, Y., & Xu, Y. (2024). Random Forest-Based Analysis of Variability in Feature Impacts. 2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA), pp. 1130–1135. https://doi.org/10.1109/icipca61593.2024.10708791
Mittal, M., Al–Jawahry, H. M., Varshney, N., Kumar, S. P., Michaelson, J. J., & Reddy, R. (2024). Improving Support Vector Machine Performance with Advanced Kernel Methods. 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), pp. 1749–1754. https://doi.org/10.1109/ic3i61595.2024.10828664
Mohd, N., Kumar, I., & Agrawal, A. J. (2023). Machine Learning Applications and the use of Big Data to Manage the Operation of SMEs. 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI), pp. 664–668. https://doi.org/10.1109/iccsai59793.2023.10421701
Mushagalusa, C. A., Fandohan, A. B., & Kakaï, R. G. (2022). Random Forests in Count Data Modelling: An Analysis of the Influence of Data Features and Overdispersion on Regression Performance. Journal of Probability and Statistics, 2022, 1–21. https://doi.org/10.1155/2022/2833537
Mustaqeem, M., & Siddiqui, T. (2023). A Hybrid Software Defects Prediction Model for Imbalance Datasets Using Machine Learning Techniques: (S-SVM Model). Journal of Autonomous Intelligence, 6(1), 559. https://doi.org/10.32629/jai.v6i1.559
Ocran, G., Yusuf, S. O., Owusu, P., Boateng, E. A., Krampah, S. O., & Paul-Adeleye, A. H. (2024). AI-driven business analytics for SMES: Unlocking value through predictive and prescriptive analytic. International Journal of Science and Research Archive, 13(1), 3009–3022. https://doi.org/10.30574/ijsra.2024.13.1.2001
Ojukwu, P. U., Omokhoa, H. E., Odionu, C. S., Azubuike, C., & Sule, A. K. (2025). Digital Transformation and Optimization Framework for Advancing SME Growth and Operational Effectiveness. International Journal of Research and Innovation in Social Science, VIII(XII), 4607–4628. https://doi.org/10.47772/ijriss.2024.8120387
Orra, A., Sahoo, K., Choudhary, H. (2023). Machine Learning-Based Hybrid Models for Trend Forecasting in Financial Instruments. In Thakur, M., Agnihotri, S., Rajpurohit, B. S., Pant, M., Deep, K., & Nagar, A. K. (eds). Soft Computing for Problem Solving. Lecture Notes in Networks and Systems, vol. 547 (pp. 337–353). Springer, Singapore. https://doi.org/10.1007/978-981-19-6525-8_26
Pai, K. R. M., Batool, M., Ravi, N., Ramteke, S. P., Shree, A. N. R., & Meenakshi, S. (2024). Enhancing Sales Forecasting and Prediction with Cutting-Edge Machine Learning Methods. 2024 Second International Conference on Data Science and Information System (ICDSIS), pp. 1–6 https://doi.org/10.1109/icdsis61070.2024.10594627
Pandey, V., Kumar, A., Gupta, S. P., Kumar, S., & Tyagi, P. (2024). Navigating Digitalization: AHP Insights for SMEs’ Strategic Transformation. International Journal of Innovative Science and Research Technology, 9(4), 693–703 https://doi.org/10.38124/ijisrt/ijisrt24apr767
Plevris, V., Solorzano, G., Bakas, N., & Ben Seghier, M. (2022). Investigation of performance metrics in regression analysis and machine learning-based prediction models. 8th European Congress on Computational Methods in Applied Sciences and Engineering. https://doi.org/10.23967/eccomas.2022.155
Shaikh, Z. P. (2024). Leveraging Artificial Intelligence for Digital Actionable Transformation of Business. In H. Taherdoost, G. Drazenovic, M. Madanchian, I. Khan, & O. Arshi (Eds), Business Transformation in the Era of Digital Disruption (pp. 181–212). IGI Global. https://doi.org/10.4018/979-8-3693-7056-8.ch007
Sharma, H., Kumar, D., Baskar, S., Shukla, D., Singh, D., Tiwari, S., & Danish, M. (2024). Enhancement of Sales Forecasting and Prediction with Machine Learning Methods. International Research Journal of Computer Science, 11(11), 641–644. https://doi.org/10.26562/irjcs.2024.v1111.02
Shukla, M. (2024). The Impact of AI on Improving the Efficiency and Accuracy of Managerial Decisions. International Journal For Science Technology And Engineering, 12(7), 830–842. https://doi.org/10.22214/ijraset.2024.63652
Sun, Y., & Li, T. (2024). A transformer-based framework for enterprise sales forecasting. PeerJ, 10, e2503. https://doi.org/10.7717/peerj-cs.2503
Tao, S., Fan, Y., & Zhang, R. (2020). A Hybrid Forecasting Model for Stock Price Prediction. CSAE ’20: Proceedings of the 4th International Conference on Computer Science and Application Engineering, pp. 1–5. https://doi.org/10.1145/3424978.3424985
Tran, C. T. (2023). Ensemble learning approaches for classification with high-dimensional data. Journal of Science and Technique, 12(01). https://doi.org/10.56651/lqdtu.jst.v12.n1.659.ict
Wong, K. K. L. (2023). Support Vector Machine. Ch. 8 (pp. 149–176) in Cybernetical Intelligence: Engineering Cybernetics with Machine Intelligence, IEEE. https://doi.org/10.1002/9781394217519.ch8
Zhao, J., Lee, C.-D., Chen, G., & Zhang, J. (2024). Research on the Prediction Application of Multiple Classification Datasets Based on Random Forest Model. 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS), pp. 156–161. https://doi.org/10.1109/icpics62053.2024.10795875
Zheng, S., & Ding, C. (2018). Minimal Support Vector Machine. ArXiv. https://dblp.uni-trier.de/db/journals/corr/corr1804.html#abs-1804-02370
Zhu, A. (2025). VMD-SVM-based Retail Product Demand Forecasting Model. Scientific Journal of Economics and Management Research, 7(2), 24–31. https://doi.org/10.54691/g7pfr887
Article Sidebar
Published
2025-12-29
Article Details
How to Cite
Purwanto, E., Prajadi Cipto Utomo, B., Permatasari, H., & Mohd, F. (2025). A Hybrid AI Model Combining RF and SVM for Accurate Sales Prediction in SMEs to Support Digital Transformation. Journal of Telecommunications and the Digital Economy, 13(4), 33-52. https://doi.org/10.18080/jtde.v13n4.1306
Issue
Section
Digital Economy & Society
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright Telecommunications Association Inc.
How to Cite
Purwanto, E., Prajadi Cipto Utomo, B., Permatasari, H., & Mohd, F. (2025). A Hybrid AI Model Combining RF and SVM for Accurate Sales Prediction in SMEs to Support Digital Transformation. Journal of Telecommunications and the Digital Economy, 13(4), 33-52. https://doi.org/10.18080/jtde.v13n4.1306
