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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JURNAL ELEKTRO JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) SABDAMAS Jurnal Ilmiah Teknik Elektro International Journal of Engineering Continuity ROUTERS: Jurnal Sistem dan Teknologi Informasi Jurnal Pengabdian Masyarakat Charitas Cylinder : Jurnal Ilmiah Teknik Mesin
Catherine Olivia Sereati
Fakultas Teknik Unika Atma Jaya Jakarta
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences

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Journal : International Journal of Engineering Continuity

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Implementation and Analysis of Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for Irrigation Abdurohman, Abdurohman; Siregar, Marsul; Olivia Sereati, Catherine; Windasari, Silviana; W. Pandjaitan, MM. Lanny
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.
Co-Authors Abdurohman, Abdurohman Adang Suwandi Ahmad Arwin Datumaya Wahyudi Sumari Bachri, Karel Octavianus Christanto, Henoch Juli Duma Kristina Yanti Ferry Rippun Gideon Manalu Ferry Rippun Gideon Manalu Hidayat, Renaldi Indriati, Kumala Jones, Danzel Maxdriel Samuel Juli Christanto, Henoch Karangga, Yusaphat Phosa Karel Octavianus Kartadinata, Budi Kartwidjaja, Maria Angela Kevin kevin Lanny W Lestari, Hesti Tri Lukas Lukas Lukas, Lukas Marten Darmawan Melisa Mulyadi Nova Eka Budiyanta Octavianus Bachri, Karel Pangestu, Evan Sudira Paul Ferdinand, Jean Pranadjaya, Egipta Radix W. C, Hilarius Rippun G. Manalu, Ferry Sandra Octaviani Silviana Windasari Siregar, Marsul Surya Permana, Rizky Tajuddin Nur Trio Adiono Veronica Windha Mahyastuty W Pandjaitan, Lanny W. Pandjaitan, MM. Lanny Wijayanti, Linda Zefanya, Maria Zenik, Andre