Analisis Sistem Kontrol Kelembapan Ruang Budidaya Berbasis ANFIS-IoT
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Jul 30, 2025
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Abstract
Pengelolaan lingkungan mikro dalam ruang budidaya tertutup memegang peranan penting dalam menjamin pertumbuhan tanaman yang optimal. Kelembapan udara merupakan salah satu parameter krusial yang sangat dipengaruhi oleh kondisi non-stasioner dan dinamis dari sistem tertutup. Oleh karena itu, dibutuhkan sistem kontrol yang adaptif dan cerdas untuk menjaga kelembapan dalam rentang ideal. Penelitian ini mengusulkan sistem kontrol kelembaban berbasis Adaptive Neuro-Fuzzy Inference System (ANFIS) yang diintegrasikan dengan data sensor Internet of Things (IoT). Dataset dikumpulkan dari ruang budidaya aktual, lalu dilakukan preprocessing, pelatihan model ANFIS selama 200 epoch, dan evaluasi menggunakan metrik RMSE dan koefisien determinasi (R²). Model ANFIS menunjukkan performa yang cukup baik pada data pelatihan dengan nilai RMSE = 1.1927 dan R² = 0.5174, namun mengalami penurunan performa pada data pengujian dengan RMSE = 2.5669 dan R² = -1.0104. Hasil ini mengindikasikan kebutuhan akan penyempurnaan model agar lebih tahan terhadap data baru dan anomali. Sistem kontrol kelembaban berbasis ANFIS-IoT menunjukkan potensi dalam mengotomatisasi pengaturan lingkungan ruang budidaya secara cerdas. Meskipun model awal memiliki keterbatasan dalam generalisasi, pendekatan ini membuka peluang pengembangan sistem prediksi dan kontrol berbasis hybrid yang lebih adaptif untuk lingkungan dinamis.
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Februariyanti, H., Khristianto, T., Jananto, A., & Nurraharjo, E. (2025). Analisis Sistem Kontrol Kelembapan Ruang Budidaya Berbasis ANFIS-IoT. Journal of Telecommunication Electronics and Control Engineering (JTECE), 7(2), 119-133. https://doi.org/10.20895/jtece.v7i2.1833
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References
M. Rizal, “Implementasi Sistem Otomatisasi Perawatan Tanaman indoor berbasis Internet of Things (IoT),” vol. 7, no. 2, pp. 935–945, Mar. 2023, doi: 10.33395/remik.v7i2.12277.
F. Irwanto et al., “IoT and fuzzy logic integration for improved substrate environment management in mushroom cultivation,” Smart Agricultural Technology, vol. 7, Mar. 2024, doi: 10.1016/j.atech.2024.100427.
V. Thomopoulos, F. Tolis, T. F. Blounas, D. Tsipianitis, and A. Kavga, “Application of Fuzzy logic and IoT in a small-scale Smart Greenhouse System,” Smart Agricultural Technology, vol. 8, Aug. 2024, doi: 10.1016/j.atech.2024.100446.
H. N. Y. Al-Talb, S. N. M. Al-Faydi, T. A. Fathi, and M. A. S. Al-Adwany, “A Fuzzy Logic IoT- Based Temperature and Humidity Control System for Smart Buildings,” International Journal of Computing and Digital Systems, vol. 13, no. 1, pp. 139–147, 2023, doi: 10.12785/ijcds/13011.
A. F. Daru, W. Adhiwibowo, and A. M. Hirzan, “Model Pemantau Kelembaban dan Irigasi Sawah Otomatis Berbasiskan Internet of Things,” Komputika Jurnal Sistem Komputer, vol. 10, pp. 119–127, 2021, doi: 10.34010/komputika.v10i2.4515.
S. Hirasawa, M. Nakatsuka, K. Masui, T. Kawanami, and K. Shirai, “Temperature and Humidity Control in Greenhouses in Desert Areas,” Agricultural Sciences, vol. 05, no. 13, pp. 1261–1268, 2014, doi: 10.4236/as.2014.513134.
Α. Σαπουνάς, Ν. Katsoulas, B. Slager, R. A. Bezemer, and C. Lelieveld, “Design, Control, and Performance Aspects of Semi-Closed Greenhouses,” Agronomy, vol. 10, no. 11, p. 1739, 2020, doi: 10.3390/agronomy10111739.
R. Liao, S. Zhang, X. Zhang, M. Wang, H. Wu, and L. Zhangzhong, “Development of smart irrigation systems based on real-time soil moisture data in a greenhouse: Proof of concept,” Agric Water Manag, vol. 245, Feb. 2021, doi: 10.1016/j.agwat.2020.106632.
M. S. Alajmi and A. M. Almeshal, “Prediction and optimization of surface roughness in a turning process using the ANFIS-QPSO method,” Materials, vol. 13, no. 13, pp. 1–23, 2020, doi: 10.3390/ma13132986.
M. Pa and A. Kazemi, “ANFIS-based prediction of power generation for combined cycle power plant.” [Online]. Available: www.power-technology.com.
N. M. Dang and D. T. Anh, “Integration of ANFIS With PCA and DWT for Daily Suspended Sediment Concentration Prediction,” Water Sa, vol. 47, no. 2 April, 2021, doi: 10.17159/wsa/2021.v47.i2.10916.
I. Ebtehaj et al., “Prediction of daily water level using new hybridized GS-GMDH and ANFIS-FCM models,” Engineering Applications of Computational Fluid Mechanics, vol. 15, no. 1, pp. 1343–1361, 2021, doi: 10.1080/19942060.2021.1966837.
A. Arora et al., “Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India,” Science of the Total Environment, vol. 750, 2021, doi: 10.1016/j.scitotenv.2020.141565.
T. H. Kim et al., “ANFIS Fuzzy convolutional neural network model for leaf disease detection,” Front Plant Sci, vol. 15, 2024, doi: 10.3389/fpls.2024.1465960.
M. A. Denai, F. Palis, and A. Zeghbib, “ANFIS Based Modelling and Control of Non-linear Systems : A tutorial *.”
M. Rajagopal, M. Ponnuchamy, and A. Kapoor, “Water management for irrigation scheduling by computing evapotranspiration using ANFIS modelling,” Desalination Water Treat, vol. 251, pp. 123–133, Mar. 2022, doi: 10.5004/dwt.2022.28290.
L. S. Kondaka, R. Iyer, S. Jaiswal, and A. Ali, “A Smart Hydroponic Farming System Using Machine Learning,” in Proceedings of the International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023, 2023, pp. 357–362. doi: 10.1109/IITCEE57236.2023.10090860.
B. Edwin et al., “Smart agriculture monitoring system for outdoor and hydroponic environments,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 25, no. 3, pp. 1679–1687, 2022, doi: 10.11591/ijeecs.v25.i3.pp1679-1687.
K. Boikanyo, A. M. Zungeru, B. Sigweni, A. Yahya, and C. Lebekwe, “Remote patient monitoring systems: Applications, architecture, and challenges,” Sci Afr, vol. 20, p. e01638, 2023, doi: https://doi.org/10.1016/j.sciaf.2023.e01638.
M. Montaño-Blacio, J. González-Escarabay, Ó. Jiménez-Sarango, L. Mingo-Morocho, and C. Carrión-Aguirre, “Design and deployment of an IoT-based monitoring system for hydroponic crops | Diseño y despliegue de un sistema de monitoreo basado en IoT para cultivos hidropónicos,” Ingenius, vol. 2023, no. 30, pp. 9–18, 2023, doi: 10.17163/ings.n30.2023.01.
R. Y. Aburasain, “Enhanced Black Widow Optimization With Hybrid Deep Learning Enabled Intrusion Detection in Internet of Things-Based Smart Farming,” IEEE Access, vol. 12, pp. 16621–16631, 2024, doi: 10.1109/ACCESS.2024.3359043.
S. W. Chin, G. Rubambiza, Y. Zhao, K. Malek, and H. Weatherspoon, “Realtime optimization and management system (ROAM): A decision support system for digital agriculture systems,” Smart Agricultural Technology, vol. 8, Aug. 2024, doi: 10.1016/j.atech.2024.100452.
A. Seifi, M. Ehteram, V. P. Singh, and A. Mosavi, “Modeling and uncertainty analysis of groundwater level using six evolutionary optimization algorithms hybridized with ANFIS, SVM, and ANN,” Sustainability (Switzerland), vol. 12, no. 10, 2020, doi: 10.3390/SU12104023.
J. Yang, C. Shang, Y. Li, F. Li, L. Shen, and Q. Shen, “Constructing ANFIS With Sparse Data Through Group-Based Rule Interpolation: An Evolutionary Approach,” IEEE Transactions on Fuzzy Systems, vol. 30, no. 4, pp. 893–907, 2022, doi: 10.1109/TFUZZ.2021.3049949.
R. Xiong et al., “Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions,” Environ Sci Technol, vol. 56, no. 14, pp. 10530–10542, 2022, doi: 10.1021/acs.est.2c02232.
M. Hamitouche and J.-L. Molina, “A Review of AI Methods for the Prediction of High-Flow Extremal Hydrology,” Water Resources Management, vol. 36, no. 10, pp. 3859–3876, 2022, doi: 10.1007/s11269-022-03240-y.
E. M. Raouhi, M. Zouizza, M. Lachgar, Y. Zouani, H. Hrimech, and A. Kartit, “AIDSII: An AI-based digital system for intelligent irrigation,” Software Impacts, vol. 17, 2023, doi: 10.1016/j.simpa.2023.100574.
S. L. Zhou, A. A. Shah, P. K. Leung, X. Zhu, and Q. Liao, “A comprehensive review of the applications of machine learning for HVAC,” DeCarbon, vol. 2, p. 100023, 2023, doi: https://doi.org/10.1016/j.decarb.2023.100023.
L. Morales Escobar, J. Aguilar, A. Garces-Jimenez, J. A. Gutierrez De Mesa, and J. M. Gomez-Pulido, “Advanced fuzzy-logic-based context-driven control for HVAC management systems in buildings,” IEEE Access, vol. 8, pp. 16111–16126, 2020, doi: 10.1109/ACCESS.2020.2966545.
H. Oubehar, A. Selmani, A. Ed‐Dahhak, A. Lachhab, M. E. H. Archidi, and B. Bouchikhi, “ANFIS-Based Climate Controller for Computerized Greenhouse System,” Advances in Science Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 8–12, 2020, doi: 10.25046/aj050102.
H. Oubeha, “Intelligent Control for an Experimental Greenhouse Climate Based on ANFIS Technology,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 1.5, pp. 84–90, 2020, doi: 10.30534/ijatcse/2020/1391.52020.
H. Hamidane et al., “Application Analysis of ANFIS Strategy for Greenhouse Climate Parameters Prediction: Internal Temperature and Internal Relative Humidity Case of Study,” E3s Web of Conferences, vol. 297, p. 01041, 2021, doi: 10.1051/e3sconf/202129701041.
L. Brahimi, N. Hadroug, A. Iratni, A. Hafaifa, and I. Colak, “Advancing predictive maintenance for gas turbines: An intelligent monitoring approach with ANFIS, LSTM, and reliability analysis,” Comput Ind Eng, vol. 191, May 2024, doi: 10.1016/j.cie.2024.110094.
M. A. Khan and F. Algarni, “A Healthcare Monitoring System for the Diagnosis of Heart Disease in the IoMT Cloud Environment Using MSSO-ANFIS,” IEEE Access, vol. 8, pp. 122259–122269, 2020, doi: 10.1109/ACCESS.2020.3006424.
F. Irwanto et al., “IoT and fuzzy logic integration for improved substrate environment management in mushroom cultivation,” Smart Agricultural Technology, vol. 7, Mar. 2024, doi: 10.1016/j.atech.2024.100427.
V. Thomopoulos, F. Tolis, T. F. Blounas, D. Tsipianitis, and A. Kavga, “Application of Fuzzy logic and IoT in a small-scale Smart Greenhouse System,” Smart Agricultural Technology, vol. 8, Aug. 2024, doi: 10.1016/j.atech.2024.100446.
H. N. Y. Al-Talb, S. N. M. Al-Faydi, T. A. Fathi, and M. A. S. Al-Adwany, “A Fuzzy Logic IoT- Based Temperature and Humidity Control System for Smart Buildings,” International Journal of Computing and Digital Systems, vol. 13, no. 1, pp. 139–147, 2023, doi: 10.12785/ijcds/13011.
A. F. Daru, W. Adhiwibowo, and A. M. Hirzan, “Model Pemantau Kelembaban dan Irigasi Sawah Otomatis Berbasiskan Internet of Things,” Komputika Jurnal Sistem Komputer, vol. 10, pp. 119–127, 2021, doi: 10.34010/komputika.v10i2.4515.
S. Hirasawa, M. Nakatsuka, K. Masui, T. Kawanami, and K. Shirai, “Temperature and Humidity Control in Greenhouses in Desert Areas,” Agricultural Sciences, vol. 05, no. 13, pp. 1261–1268, 2014, doi: 10.4236/as.2014.513134.
Α. Σαπουνάς, Ν. Katsoulas, B. Slager, R. A. Bezemer, and C. Lelieveld, “Design, Control, and Performance Aspects of Semi-Closed Greenhouses,” Agronomy, vol. 10, no. 11, p. 1739, 2020, doi: 10.3390/agronomy10111739.
R. Liao, S. Zhang, X. Zhang, M. Wang, H. Wu, and L. Zhangzhong, “Development of smart irrigation systems based on real-time soil moisture data in a greenhouse: Proof of concept,” Agric Water Manag, vol. 245, Feb. 2021, doi: 10.1016/j.agwat.2020.106632.
M. S. Alajmi and A. M. Almeshal, “Prediction and optimization of surface roughness in a turning process using the ANFIS-QPSO method,” Materials, vol. 13, no. 13, pp. 1–23, 2020, doi: 10.3390/ma13132986.
M. Pa and A. Kazemi, “ANFIS-based prediction of power generation for combined cycle power plant.” [Online]. Available: www.power-technology.com.
N. M. Dang and D. T. Anh, “Integration of ANFIS With PCA and DWT for Daily Suspended Sediment Concentration Prediction,” Water Sa, vol. 47, no. 2 April, 2021, doi: 10.17159/wsa/2021.v47.i2.10916.
I. Ebtehaj et al., “Prediction of daily water level using new hybridized GS-GMDH and ANFIS-FCM models,” Engineering Applications of Computational Fluid Mechanics, vol. 15, no. 1, pp. 1343–1361, 2021, doi: 10.1080/19942060.2021.1966837.
A. Arora et al., “Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India,” Science of the Total Environment, vol. 750, 2021, doi: 10.1016/j.scitotenv.2020.141565.
T. H. Kim et al., “ANFIS Fuzzy convolutional neural network model for leaf disease detection,” Front Plant Sci, vol. 15, 2024, doi: 10.3389/fpls.2024.1465960.
M. A. Denai, F. Palis, and A. Zeghbib, “ANFIS Based Modelling and Control of Non-linear Systems : A tutorial *.”
M. Rajagopal, M. Ponnuchamy, and A. Kapoor, “Water management for irrigation scheduling by computing evapotranspiration using ANFIS modelling,” Desalination Water Treat, vol. 251, pp. 123–133, Mar. 2022, doi: 10.5004/dwt.2022.28290.
L. S. Kondaka, R. Iyer, S. Jaiswal, and A. Ali, “A Smart Hydroponic Farming System Using Machine Learning,” in Proceedings of the International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023, 2023, pp. 357–362. doi: 10.1109/IITCEE57236.2023.10090860.
B. Edwin et al., “Smart agriculture monitoring system for outdoor and hydroponic environments,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 25, no. 3, pp. 1679–1687, 2022, doi: 10.11591/ijeecs.v25.i3.pp1679-1687.
K. Boikanyo, A. M. Zungeru, B. Sigweni, A. Yahya, and C. Lebekwe, “Remote patient monitoring systems: Applications, architecture, and challenges,” Sci Afr, vol. 20, p. e01638, 2023, doi: https://doi.org/10.1016/j.sciaf.2023.e01638.
M. Montaño-Blacio, J. González-Escarabay, Ó. Jiménez-Sarango, L. Mingo-Morocho, and C. Carrión-Aguirre, “Design and deployment of an IoT-based monitoring system for hydroponic crops | Diseño y despliegue de un sistema de monitoreo basado en IoT para cultivos hidropónicos,” Ingenius, vol. 2023, no. 30, pp. 9–18, 2023, doi: 10.17163/ings.n30.2023.01.
R. Y. Aburasain, “Enhanced Black Widow Optimization With Hybrid Deep Learning Enabled Intrusion Detection in Internet of Things-Based Smart Farming,” IEEE Access, vol. 12, pp. 16621–16631, 2024, doi: 10.1109/ACCESS.2024.3359043.
S. W. Chin, G. Rubambiza, Y. Zhao, K. Malek, and H. Weatherspoon, “Realtime optimization and management system (ROAM): A decision support system for digital agriculture systems,” Smart Agricultural Technology, vol. 8, Aug. 2024, doi: 10.1016/j.atech.2024.100452.
A. Seifi, M. Ehteram, V. P. Singh, and A. Mosavi, “Modeling and uncertainty analysis of groundwater level using six evolutionary optimization algorithms hybridized with ANFIS, SVM, and ANN,” Sustainability (Switzerland), vol. 12, no. 10, 2020, doi: 10.3390/SU12104023.
J. Yang, C. Shang, Y. Li, F. Li, L. Shen, and Q. Shen, “Constructing ANFIS With Sparse Data Through Group-Based Rule Interpolation: An Evolutionary Approach,” IEEE Transactions on Fuzzy Systems, vol. 30, no. 4, pp. 893–907, 2022, doi: 10.1109/TFUZZ.2021.3049949.
R. Xiong et al., “Predicting Dynamic Riverine Nitrogen Export in Unmonitored Watersheds: Leveraging Insights of AI from Data-Rich Regions,” Environ Sci Technol, vol. 56, no. 14, pp. 10530–10542, 2022, doi: 10.1021/acs.est.2c02232.
M. Hamitouche and J.-L. Molina, “A Review of AI Methods for the Prediction of High-Flow Extremal Hydrology,” Water Resources Management, vol. 36, no. 10, pp. 3859–3876, 2022, doi: 10.1007/s11269-022-03240-y.
E. M. Raouhi, M. Zouizza, M. Lachgar, Y. Zouani, H. Hrimech, and A. Kartit, “AIDSII: An AI-based digital system for intelligent irrigation,” Software Impacts, vol. 17, 2023, doi: 10.1016/j.simpa.2023.100574.
S. L. Zhou, A. A. Shah, P. K. Leung, X. Zhu, and Q. Liao, “A comprehensive review of the applications of machine learning for HVAC,” DeCarbon, vol. 2, p. 100023, 2023, doi: https://doi.org/10.1016/j.decarb.2023.100023.
L. Morales Escobar, J. Aguilar, A. Garces-Jimenez, J. A. Gutierrez De Mesa, and J. M. Gomez-Pulido, “Advanced fuzzy-logic-based context-driven control for HVAC management systems in buildings,” IEEE Access, vol. 8, pp. 16111–16126, 2020, doi: 10.1109/ACCESS.2020.2966545.
H. Oubehar, A. Selmani, A. Ed‐Dahhak, A. Lachhab, M. E. H. Archidi, and B. Bouchikhi, “ANFIS-Based Climate Controller for Computerized Greenhouse System,” Advances in Science Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 8–12, 2020, doi: 10.25046/aj050102.
H. Oubeha, “Intelligent Control for an Experimental Greenhouse Climate Based on ANFIS Technology,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 1.5, pp. 84–90, 2020, doi: 10.30534/ijatcse/2020/1391.52020.
H. Hamidane et al., “Application Analysis of ANFIS Strategy for Greenhouse Climate Parameters Prediction: Internal Temperature and Internal Relative Humidity Case of Study,” E3s Web of Conferences, vol. 297, p. 01041, 2021, doi: 10.1051/e3sconf/202129701041.
L. Brahimi, N. Hadroug, A. Iratni, A. Hafaifa, and I. Colak, “Advancing predictive maintenance for gas turbines: An intelligent monitoring approach with ANFIS, LSTM, and reliability analysis,” Comput Ind Eng, vol. 191, May 2024, doi: 10.1016/j.cie.2024.110094.
M. A. Khan and F. Algarni, “A Healthcare Monitoring System for the Diagnosis of Heart Disease in the IoMT Cloud Environment Using MSSO-ANFIS,” IEEE Access, vol. 8, pp. 122259–122269, 2020, doi: 10.1109/ACCESS.2020.3006424.