Optimasi Nutrisi Melon Berbasis Internet of Things dan Machine Learning pada Greenhouse - Sazira, Radeb; Muhammad Rakha; Djahra Rachmawaty; Dandi Novian Pratama; Laras Desfiyanti; Muhammad Rafi Riza Pratama; Inna Novianty; Hafiedh Adi Nugroho (Jurnal Teknologi Komputer dan Informasi, 2026)
Melon (Cucumis melo L.) is a horticultural commodity with high economic value; however, its production stability still faces several constraints, particularly in nutrient management. This study aimed to develop a nutrient optimization system based on the Internet of Things (IoT) and machine learning to determine precise nutrient dosage for melon cultivation. The research employed the Research and Development (RnD) method by designing a system using an ESP32 microcontroller integrated with pH, Total Dissolved Solids (TDS), and microclimate sensors in a greenhouse environment. The Random Forest algorithm was applied to analyze data and accurately predict plant nutrient requirements. System testing was conducted in the BRMP SDLP greenhouse through the integration of hardware and a website platform for remote monitoring and control. The results showed that the system was capable of providing precise nutrient dosage predictions during both vegetative and generative growth phases of melon plants. The developed machine learning model achieved an accuracy level of 90%, while sensor calibration results ranged from 85–90% accuracy. The developed system supports more efficient melon cultivation through real-time nutrient monitoring and control.