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All Journal International Journal of Engineering Continuity
Abdurohman Abdurohman
Graduate School of Electrical Engineering, School of Bioscience, Technology and Innovation (SBTI), Atma Jaya Catholic University of Indonesia, Jakarta, Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Implementation and Analysis of Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for Irrigation Abdurohman Abdurohman; Marsul Siregar; Catherine Olivia Sereati; Silviana Windasari; MM. Lanny W. Pandjaitan
International Journal of Engineering Continuity Vol. 4 No. 1 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i1.399

Abstract

Efficient water management in agriculture is crucial due to dynamic environmental conditions and increasing resource scarcity. Fuzzy Inference System (FIS) is widely applied in irrigation control for its ability to handle uncertaintys using rule-based domain knowledge. However, conventional FIS lacks adaptability to environmental changes, limiting its long-term accuracy and responsiveness. Adaptive Neuro-Fuzzy Inference System (ANFIS) addresses this limitation by combining fuzzy logic with neural network learning, enabling automatic adjustment of model parameters based on data patterns. This study compares the performance of FIS and ANFIS in predicting optimal irrigation levels based on soil moisture, air temperature, relative humidity, and solar radiation. A synthetic dataset of 1,000 samples simulating realistic agricultural conditions was generated and normalized to improve computational consistency. The FIS model uses triangular membership functions and five expert-defined fuzzy rules, while ANFIS employs Gaussian membership functions with parameters optimized using the ADAM algorithm over 50 training epochs. Results show that ANFIS outperforms FIS, lowering RMSE from 0.13 to 0.07, halving MAE from 0.10 to 0.05, and increasing R² from 0.85 to 0.93, indicating a substantially better predictive performance. This study demonstrates that ANFIS is more adaptive, accurate, and computationally efficient, contributing to the advancement of intelligent and sustainable irrigation systems in precision agriculture.
Optimization Model of IoT and Machine Learning for Renewable Energy-Powered Aeroponic Systems Silviana Windasari; Abdurohman Abdurohman; Adi Affandi Ratib; Ade Frihadi; Khalid Montazi
International Journal of Engineering Continuity Vol. 4 No. 2 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i2.426

Abstract

This study proposes an optimization model integrating Internet of Things (IoT) and Machine Learning (ML) for renewable energy-powered aeroponic systems as a conceptual framework to enhance sustainable agriculture and address global food security challenges. The model is designed to mitigate land degradation, water scarcity, and the impacts of climate variability on crop productivity. It combines IoT-based real-time monitoring of key environmental variables temperature, humidity, pH, electrical conductivity, and light intensity with Long Short-Term Memory (LSTM) networks for time-series prediction of crop growth and resource requirements. Renewable energy sources, particularly solar photovoltaic systems with battery storage, ensure reliable and environmentally friendly power supply. The proposed approach emphasizes predictive optimization, where IoT data streams inform adaptive LSTM algorithms for precise irrigation and nutrient control. Model performance is evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²). Although the study remains conceptual and simulation-based, validation results demonstrate high predictive accuracy and efficiency. This research establishes a foundational framework for subsequent prototype development, experimental validation, and techno-economic evaluation toward scalable, energy-efficient, and sustainable smart farming systems.
Co-Authors Ade Frihadi Adi Affandi Ratib Catherine Olivia Sereati Khalid Montazi Marsul Siregar MM. Lanny W. Pandjaitan Silviana Windasari