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Design of a Miniature Sensor and Algorithm for Real-Time Interpretation of Micro-Nutrient Data Julfikar Mawansyah; Muhammad Wardhani; Lita Budiarti
JURNAL RISET RUMPUN ILMU TEKNIK Vol. 4 No. 2 (2025): Agustus : Jurnal Riset Rumpun Ilmu Teknik
Publisher : Pusat riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurritek.v4i2.5812

Abstract

The increasing demand for sustainable agricultural practices has led to the adoption of hydroponics, a method of growing plants in nutrient-rich solutions without soil. This method is particularly effective in controlled environments where resource efficiency is paramount. However, the success of hydroponic systems depends heavily on precise nutrient management, especially for micro-nutrients, which are crucial for plant health and productivity. Traditional methods of nutrient monitoring are often labor-intensive and lack the real-time responsiveness needed for optimal nutrient control. This study addresses the challenge of real-time nutrient management in hydroponic systems by developing a miniature sensor system integrated with Internet of Things (IoT) technology. The proposed system is designed to detect micro-nutrient concentrations accurately and transmit data in real-time to a cloud platform for continuous monitoring and automated control. Advanced algorithms are employed for data processing and calibration, ensuring high accuracy in detecting micro-nutrient levels. The system was tested in a controlled hydroponic environment, where it demonstrated high accuracy with minimal error margins, validated by a consistently low Mean Absolute Error (MAE). The integration of IoT allowed for seamless data transmission and real-time analysis, enabling immediate adjustments to nutrient levels as needed. This research contributes to the advancement of precision agriculture by providing an effective solution for real-time nutrient management in hydroponic systems, potentially improving crop yields and resource efficiency.
AI, Agriculture, and Decolonial Perspectives: Recognizing Local Knowledge for Sustainability Julfikar Mawansyah; Mokh. Sholihul Hadi; Syaad Patmanthara
Jurnal Riset Rumpun Agama dan Filsafat Vol. 4 No. 3 (2025): Desember: Jurnal Riset Rumpun Agama dan Filsafat
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurrafi.v4i3.7250

Abstract

This study explores the intersection of Artificial Intelligence (AI), agriculture, and decolonial philosophy, emphasizing the role of local knowledge as the foundation for sustainable agricultural technology in Indonesia. The research investigates how AI can be developed not as a tool of technological domination but as a dialogical partner that recognizes the epistemic value of indigenous wisdom. Using a mixed-method approach, the study combines algorithmic experiments applying lightweight Convolutional Neural Networks (CNN) with Explainable AI (XAI) methods such as SHAP and LIME with participatory interviews involving farmers in Bima District. Empirical findings show that models integrated with localized visualization and community-based interpretability improved user trust by 84% and reduced computational energy by 28% without compromising accuracy. More importantly, the interaction between AI and farmers revealed a form of epistemic integration where algorithmic logic aligns with traditional indicators, such as soil texture, humidity, and seasonal signs known to local farmers. Philosophically, this research asserts that sustainable AI should emerge from ecological and cultural contexts rather than imposing external frameworks. In the decolonial sense, it positions local farmers not as passive users but as active epistemic agents shaping the meaning of technology. Thus, AI becomes not only a technical instrument but a site of ethical and epistemic liberation that reaffirms human responsibility toward knowledge, culture, and the earth.