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p-Index From 2021 - 2026
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All Journal Indonesian Journal of Data and Science
Muh Mu'min
Universitas Tomakaka
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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An IoT-Based Fuzzy Decision Support System for Rhizobium Inoculation to Improve Soybean Productivity Andi Ulfah Tenripada; Lukman Syafie; Muh Mu'min; Raqhib Ataillah; Muh Nawir
Indonesian Journal of Data and Science Vol. 7 No. 2 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i2.458

Abstract

Introduction: Soybean productivity in Indonesia remains below national demand, while the effectiveness of Rhizobium inoculation depends strongly on dynamic soil conditions such as pH, moisture, temperature, and nitrogen availability. This study develops an Internet of Things (IoT)-based decision support system for adaptive Rhizobium inoculation in highland soybean cultivation. Method: The system integrates a Soil NPK RS485 Modbus sensor, ESP32-WROOM-32D microcontroller, MQTT communication, and the SMARTO web dashboard to monitor four environmental parameters in real time. A Mamdani Fuzzy Inference System was implemented using 17 membership functions and 25 expert-derived IF–THEN rules, with centroid defuzzification producing Rhizobium dose recommendations from 0 to 200 g/ha. Results and Discussion: Field readings of pH 7.5, soil moisture 55%, temperature 26°C, and nitrogen 155 mg/kg generated a recommendation of 16.56 g/ha, classified as Very Low, with a pump volume of 33 mL/ha. Expert validation across 30 simulated highland scenarios produced an overall agreement rate of 83.3%, demonstrating satisfactory consistency between system recommendations and agronomic judgment. Conclusion: The proposed IoT-Fuzzy DSS demonstrates the feasibility of location-specific, real-time, and interpretable Rhizobium inoculation support for highland soybean cultivation, providing a practical foundation for precision biological-input management. 
Co-Authors A. Ulfah Tenripada Lukman Syafie Muh Nawir Raqhib Ataillah