An AI-Driven Digital Platform for Predicting Coffee Acidity from Environmental Variables: Evidence from Bandung Regency
Keywords:
Coffee, Acidity (pH), Environmental Factors, Gradient Boosting, Bandung RegencyAbstract
Despite the importance of environmental control in specialty coffee production, predictive and data-driven systems that connect environmental conditions with coffee acidity remain limited, particularly in smallholder farming contexts. This study aims to design and implement a digital platform based on the Gradient Boosting algorithm to analyze the relationship between environmental factors (soil, topography, and climate) and coffee acidity (pH) levels in Bandung Regency. Through a machine learning approach, the developed model successfully captures complex non-linear relationships between environmental variables and coffee acidity, achieving a high level of accuracy (R² = 0.95) and reducing the RMSE from 0.077 to 0.040 within five learning iterations. The most influential environmental factor affecting coffee acidity was altitude (0.42), followed by soil pH (0.25) and rainfall (0.18). The predictive model was integrated into a web-based system (KopiAsa), allowing farmers to input environmental data and obtain real-time acidity predictions. This platform functions as a data-driven decision-support tool that enhances analysis efficiency, optimizes farm management, and strengthens the competitiveness of local coffee. The results accelerate the digital transformation of the agricultural sector by supporting precision agriculture and promoting more sustainable coffee production practices in Bandung Regency.
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