Authors : Sheilu Amor Q. Wawa

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/5xnpwase

DOI : https://doi.org/10.38124/ijisrt/26may1576

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Arabica coffee is one of the most economically significant agricultural commodities worldwide, with production influenced by environmental, agronomic, and socio-economic factors. Accurate prediction of coffee yield is essential for improving productivity and sustainability in the agricultural sector. This study adopts a data mining approach to predict Arabica coffee production using two machine learning algorithms: Linear Regression and K-Nearest Neighbor (KNN). The study utilizes secondary agricultural datasets and applies data preprocessing techniques, including cleaning, normalization, and feature selection. Predictive models were developed and evaluated using standard performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Findings reveal that both algorithms can effectively model coffee production patterns, with variations in performance depending on data distribution and complexity. The results demonstrate the potential of data mining techniques in enhancing agricultural forecasting and supporting data-driven decision-making for coffee production management.

Keywords : Arabica Coffee Production, Machine Learning, Linear Regression, K-Nearest Neighbor, Predictive Analytics.

References :

• Journal Article with DOI

1. Bunn, C., Läderach, P., Rivera, O., & Kirschke, D. (2015). A bitter cup: Climate change profile of global production of Arabica and Robusta coffee. Climatic Change, 129(1–2), 89–101. https://doi.org/10.1007/s10584-014-1306-x
2. Duarte, A., Uribe, J. C., Sarache, W., & Calderón, A. J. E. (2019). Economic, environmental, and social assessment of bioethanol production using multiple coffee crop residues. Energies, 12(2), 332. https://doi.org/10.3390/en12020332
3. Effendi, D., & Rismaya, M. (2020). Design and development of coffee production information system to support coffee production productivity in farmers group. IOP Conference Series: Materials Science and Engineering, 725(1), 012052. https://doi.org/10.1088/1757-899X/725/1/012052
4. Kittichotsatsawat, Y., Jangkrajrang, V., & Tippayawong, K. Y. (2020). Enhancing coffee supply chain towards sustainable growth with big data and modern agricultural technologies. Sustainability, 12(4), 1634. https://doi.org/10.3390/su12041634
5. Torok, A., Mizik, T., & Jambor, A. (2019). The competitiveness of global coffee trade. Journal of International Studies, 12(2), 127–141. https://doi.org/10.14254/2071-8330.2019/12-2/8

• Journal Article Without DOI (When DOI is not Available)

6. Çinar, Z. M., et al. (2020). Machine learning in predictive maintenance toward sustainable smart manufacturing in the industry. International Journal of Engineering and Issues, 4(6), 150–156.
7. Saragih, J. R. (2013). Socioeconomic and ecological dimension of certified and conventional Arabica coffee production in North Sumatra, Indonesia. Asian Journal of Agriculture and Rural Development, 3(3), 93–107.

• Online Discussion Paper

8. Lewin, B., Giovannucci, D., & Varangis, P. (2004). Coffee markets: New paradigms in global supply and demand. World Bank Agriculture and Rural Development Discussion Paper.

• Online Website

9. International Coffee Organization. (n.d.). Total production by all exporting countries. Retrieved from https://www.ico.org/prices/po-production.pdf