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All Journal G-Tech : Jurnal Teknologi Terapan Brilliance: Research of Artificial Intelligence
Ghiyalti Novilia
Politeknik Negeri Lhokseumawe, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering Mathematics Other

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Design and Implementation of a Smart Garden System for Monitoring and Automatic Watering of Maidenhair Fern Using the Blynk Platform Ghiyalti Novilia; Yuli Mauliza; Arsy Febrina Dewi; Teuku Afriliansyah
G-Tech: Jurnal Teknologi Terapan Vol 10 No 1 (2026): G-Tech, Vol. 10 No. 1 January 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i1.8824

Abstract

Maidenhair fern (Adiantum tenerum Sw.) is an ornamental plant with high economic value but is highly sensitive to environmental conditions, especially soil moisture, which must be maintained within 50%–80% for optimal growth. Improper cultivation, including poor drainage and fluctuating soil moisture, can cause leaf wilting, root rot, and plant death. This study uses a system design and experimental testing approach to develop a smart garden system for monitoring and automatic watering of maidenhair fern using the Blynk platform. The system integrates an ESP32 microcontroller, soil moisture sensor, relay module, water pump, and Wi-Fi connectivity for real-time monitoring and remote control. Soil moisture conditions were classified as dry (0–49%), moist (50–80%), and wet (80–100%). Results show that the pump activates below 50% and deactivates above 80%, with dry soil (42%–49%) increasing to wet levels (89%–95%) after irrigation. Relay testing confirmed that a high input consistently turns the pump on under dry conditions, while a low input turns it off under moist or wet conditions. Multi-day performance tests demonstrated stable, reliable operation, with real-time data displayed on both LCD and the Blynk application. These findings indicate that the smart garden system effectively regulates soil moisture, simplifies maintenance of maidenhair fern, and provides a practical foundation for IoT-based smart agriculture applications.
Performance Comparison of Fuzzy-PID and MPC for Cascade Water Tank Control Systems Yuli Mauliza; Ghiyalti Novilia; Ibnu Khaldun; Muhammad Azzahari; M. Basyir
Brilliance: Research of Artificial Intelligence Vol. 6 No. 3 (2026): Brilliance: Research of Artificial Intelligence, Article Research August 2026
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v6i3.9541

Abstract

Level and flow control systems are widely used in industrial processes, where poor control performance may cause process instability, energy inefficiency, and reduced product quality. This study aims to design and compare the performance of Model Predictive Control and Fuzzy-PID in a cascade level-flow control system based on models obtained through system identification. An experimental simulation approach was conducted, including system identification, process modeling, controller design, and performance evaluation. Black-box modeling was performed using bump test data processed in the MATLAB System Identification Toolbox, producing level and flow models with average fit estimations of 87.54% and 85.19%, respectively. The identified models were used to develop both controllers. MPC was designed with a prediction horizon of 40, a control horizon of 3, and a sampling time of 0.01 s, while Fuzzy-PID employed a fuzzy inference system to tune PID parameters based on error and change of error. Simulation results show that both controllers achieved the desired setpoint. However, MPC provided superior performance with no overshoot, a settling time of approximately 8 s, and a steady-state error of 0.0001. In contrast, Fuzzy-PID produced 24% overshoot, a settling time of 28 s, and oscillatory responses under disturbances. These results indicate that MPC is more effective and reliable for cascade level-flow control applications in industrial systems.
Co-Authors Afriliansyah, Teuku Arsy Febrina Dewi Ibnu Khaldun M. Basyir Muhammad Azzahari Yuli Mauliza