Optimized ensemble framework for predicting hydroponic stock and sales using machine learning
| Dublin Core | PKP Metadata Items | Metadata for this Document | |
| 1. | Title | Title of document | Optimized ensemble framework for predicting hydroponic stock and sales using machine learning |
| 2. | Creator | Author's name, affiliation, country | Viktor Handrianus Pranatawijaya; University of Palangka Raya; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Ressa Priskila; University of Palangka Raya; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Putu Bagus Adidyana Anugrah Putra; University of Palangka Raya; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Nova Noor Kamala Sari; University of Palangka Raya; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Efrans Christian; University of Palangka Raya; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Septian Geges; University of Palangka Raya; Indonesia |
| 2. | Creator | Author's name, affiliation, country | Novera Kristianti; University of Palangka Raya; Indonesia |
| 3. | Subject | Discipline(s) | Artificial Intelligence; Machine Learning; Linear Regression; Random Forest; Extreme Radient Bosting; Ensemble Model; Evolutionary Algorithm; LIME; Sustainable Development Goals; |
| 3. | Subject | Keyword(s) | Evolutionary algorithm; Hydroponic farming; Optimized ensemble model; Predictive analytics; Sustainable agriculture |
| 4. | Description | Abstract | The increasing global demand for food necessitates the adoption of sustainable agricultural practices. Hydroponic farming, while efficient in resource utilization, faces challenges in accurately predicting stock levels and sales due to dynamic, ever-changing factors. This research presents an optimized ensemble framework for forecasting hydroponic stock levels and sales by integrating linear regression (LR), random forest (RF), and XGBoost, further enhanced through an evolutionary algorithm (EA). The proposed framework is evaluated using root mean square error (RMSE) and mean absolute error (MAE), demonstrating significant accuracy improvements over individual models. The ensemble model achieves an RMSE reduction of 43.82% for stock prediction and 55.3% for sales forecasting compared to the best-performing individual model. Additionally, local interpretable model-agnostic explanations (LIME) are employed to offer stakeholders clear insights into decision-making processes, such as identifying "number of harvested crops" and "sales data" as key drivers of prediction outcomes. This framework supports sustainable development goals (SDGs) 9.3, 12.3, and 12.C by promoting resource efficiency, reducing food waste, and improving small-scale farmer market access. Future research will explore real-time data integration for dynamic adaptation and further model enhancements. |
| 5. | Publisher | Organizing agency, location | Institute of Advanced Engineering and Science |
| 6. | Contributor | Sponsor(s) | |
| 7. | Date | (YYYY-MM-DD) | 2025-10-01 |
| 8. | Type | Status & genre | Peer-reviewed Article |
| 8. | Type | Type | |
| 9. | Format | File format | |
| 10. | Identifier | Uniform Resource Identifier | https://ijai.iaescore.com/index.php/IJAI/article/view/26134 |
| 10. | Identifier | Digital Object Identifier (DOI) | http://doi.org/10.11591/ijai.v14.i5.pp3879-3886 |
| 11. | Source | Title; vol., no. (year) | IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 5: October 2025 |
| 12. | Language | English=en | en |
| 14. | Coverage | Geo-spatial location, chronological period, research sample (gender, age, etc.) | |
| 15. | Rights | Copyright and permissions |
Copyright (c) 2025 Viktor Handrianus Pranatawijaya, Ressa Priskila, Putu Bagus Adidyana Anugrah Putra, Nova Noor Kamala Sari, Efrans Christian, Septian Geges, Novera Kristianti This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. |