Sistem Kendali Mikroklimat Greenhouse Cerdas Berbasis Hybrid GA-LSTM dan Fuzzy Logic Controller
Article Sidebar
📅
Published:
May 10, 2026
Main Article Content
Abstract
Article Summary
Controlling temperature and humidity in greenhouses is a complex challenge due to its non-linear nature and dependence on external weather conditions. Conventional control methods often experience energy inefficiency and delayed responses to drastic changes. This study proposes a hybrid approach by combining Long Short-Term Memory (LSTM) optimized using Genetic Algorithm (GA) for temperature prediction, and Fuzzy Logic Controller (FLC) for actuator decision-making. Genetic Algorithm is employed to find optimal hyperparameters (number of neurons and batch size) in the LSTM architecture. Experimental results demonstrate the GA-LSTM model's capability in predicting temperature with high accuracy, yielding an R² score of 0.9881 and Root Mean Square Error (RMSE) of 1.1273°C. These accurate predictions are subsequently used as input to the FLC to regulate exhaust fan speed and mist pump status. Simulations demonstrate the system's capability in making energy-efficient decisions—activating actuators only when conditions are predicted to deviate from ideal values—while remaining responsive to extreme temperature anomalies.
Keywords
Article Keywords
Downloads
Download data is not yet available.
Article Details
How to Cite
Royali, R. M., & Ariatmanto, D. (2026). Sistem Kendali Mikroklimat Greenhouse Cerdas Berbasis Hybrid GA-LSTM dan Fuzzy Logic Controller. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 7(2), 229-236. https://doi.org/10.63447/jimik.v7i2.1798
Issue
Section
Articles
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-BY 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
License: All articles are licensed under 
References
Abdullah, N., Duran, N. A., & Shari, M. F. (2021). Towards smart agriculture monitoring using fuzzy systems. IEEE Access, 9, 4097–4111. https://doi.org/10.1109/ACCESS.2020.3041597
Ajani, O. S., Uyeh, D. D., Aboyeji, E., Ha, Y., & Mallipeddi, R. (2023). A genetic programming-based optimal sensor placement for greenhouse monitoring and control. Frontiers in Plant Science, 14, Article 1152036. https://doi.org/10.3389/fpls.2023.1152036
Al-Mahdi, M. I., Sofwan, A., & Sumardi. (2021). Perancangan sistem kontrol suhu lingkungan pada smart greenhouse menggunakan metode fuzzy logic Sugeno. Transient: Jurnal Ilmiah Teknik Elektro, 10(1), 244–251. https://doi.org/10.14710/transient.v10i1.244-251
Ananda, Y., Ichsan, M. H. H., & Budi, A. S. (2023). Sistem kontrol dan monitoring prototype smart green house pada tanaman stroberi menggunakan logika fuzzy berbasis aplikasi Cayenne. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 7(2), 991–1002.
Boonman, M. (2021). Greenhouse sensor data (10 minute interval) [Data set]. Kaggle. https://www.kaggle.com/datasets/marcelboonman/greenhouse-sensor-data-10-minute-interval
Febriansyah, L. A. (2025). Implementasi fuzzy logic controller pada sistem kendali suhu dan kelembapan greenhouse tanaman stroberi. Theta Omega: Journal of Electrical Engineering, Computer, and Information Technology, 5(2).
Finecomess, S. A., Gebresenbet, G., & Alwan, H. M. (2024). Utilizing an Internet of Things (IoT) device, intelligent control design, and simulation for an agricultural system. Internet of Things, 5(1), 57–78. https://doi.org/10.3390/iot5010004
Gong, L., Yu, M., & Kollias, S. (2023). Optimizing crop yield and reducing energy consumption in greenhouse control using PSO-MPC algorithm. Algorithms, 16(5), Article 232. https://doi.org/10.3390/a16050232
Khafajeh, H., Banakar, A., Minaei, S., & Delavar, M. (2023). A hydroponic greenhouse fuzzy control system: Design, development and optimization using the genetic algorithm. Spanish Journal of Agricultural Research, 21(1), Article e0206. https://doi.org/10.5424/sjar/2023211-19392
Kurniawan, D. (2021). Prototype of control and monitor system with fuzzy logic method for smart greenhouse. Indonesian Journal of Information Systems, 3(2), 116–127. https://doi.org/10.24002/ijis.v3i2.4333
Qohar, A. L., & Suharjito. (2022). Smart agriculture for optimizing photosynthesis using internet of things and fuzzy logic. International Journal of Electrical and Computer Engineering, 12(5), 5467–5480. https://doi.org/10.11591/ijece.v12i5.pp5467-5480
Riahi, J., Nasri, H., Mami, A., & Vergura, S. (2024). Effectiveness of the fuzzy logic control to manage the microclimate inside a smart insulated greenhouse. Smart Cities, 7(3), 1304–1329. https://doi.org/10.3390/smartcities7030055
Saha, G., Shahrin, F., Khan, F. H., Meshkat, M. M., & Azad, A. A. M. (2025). Smart IoT-driven precision agriculture: Land mapping, crop prediction, and irrigation system. PLOS ONE, 20(3), Article e0319268. https://doi.org/10.1371/journal.pone.0319268
Sari, I. Y., & Wibowo, A. (2025). Sistem kendali suhu greenhouse berbasis Arduino dengan metode histeresis dan override kelembapan tanah. Jurnal Ilmiah MATRIK, 27(3), 233–241.
Subahi, A. F., & Bouazza, K. E. (2020). An intelligent IoT-based system design for controlling and monitoring greenhouse temperature. IEEE Access, 8, 125488–125500. https://doi.org/10.1109/ACCESS.2020.3007955
Thomopoulos, V., Tolis, F., Blounas, T.-F., Tsipianitis, D., & Kavga, A. (2024). Application of fuzzy logic and IoT in a small-scale smart greenhouse system. Smart Agricultural Technology, 8, Article 100446. https://doi.org/10.1016/j.atech.2024.100446
Tian, J., Li, D., & Jia, X. (2022). IoT smart agriculture and agricultural product income insurance participant behavior based on fuzzy neural network. Computational Intelligence and Neuroscience, 2022, Article 4778975. https://doi.org/10.1155/2022/4778975
Wardhani, R. N., & Shafira, A. A. (2021). Pemodelan simulasi sistem kontrol temperatur berbasis fuzzy logic pada automatic greenhouse. Dalam Seminar Nasional Teknik Elektro (hlm. 208–215).
Ajani, O. S., Uyeh, D. D., Aboyeji, E., Ha, Y., & Mallipeddi, R. (2023). A genetic programming-based optimal sensor placement for greenhouse monitoring and control. Frontiers in Plant Science, 14, Article 1152036. https://doi.org/10.3389/fpls.2023.1152036
Al-Mahdi, M. I., Sofwan, A., & Sumardi. (2021). Perancangan sistem kontrol suhu lingkungan pada smart greenhouse menggunakan metode fuzzy logic Sugeno. Transient: Jurnal Ilmiah Teknik Elektro, 10(1), 244–251. https://doi.org/10.14710/transient.v10i1.244-251
Ananda, Y., Ichsan, M. H. H., & Budi, A. S. (2023). Sistem kontrol dan monitoring prototype smart green house pada tanaman stroberi menggunakan logika fuzzy berbasis aplikasi Cayenne. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 7(2), 991–1002.
Boonman, M. (2021). Greenhouse sensor data (10 minute interval) [Data set]. Kaggle. https://www.kaggle.com/datasets/marcelboonman/greenhouse-sensor-data-10-minute-interval
Febriansyah, L. A. (2025). Implementasi fuzzy logic controller pada sistem kendali suhu dan kelembapan greenhouse tanaman stroberi. Theta Omega: Journal of Electrical Engineering, Computer, and Information Technology, 5(2).
Finecomess, S. A., Gebresenbet, G., & Alwan, H. M. (2024). Utilizing an Internet of Things (IoT) device, intelligent control design, and simulation for an agricultural system. Internet of Things, 5(1), 57–78. https://doi.org/10.3390/iot5010004
Gong, L., Yu, M., & Kollias, S. (2023). Optimizing crop yield and reducing energy consumption in greenhouse control using PSO-MPC algorithm. Algorithms, 16(5), Article 232. https://doi.org/10.3390/a16050232
Khafajeh, H., Banakar, A., Minaei, S., & Delavar, M. (2023). A hydroponic greenhouse fuzzy control system: Design, development and optimization using the genetic algorithm. Spanish Journal of Agricultural Research, 21(1), Article e0206. https://doi.org/10.5424/sjar/2023211-19392
Kurniawan, D. (2021). Prototype of control and monitor system with fuzzy logic method for smart greenhouse. Indonesian Journal of Information Systems, 3(2), 116–127. https://doi.org/10.24002/ijis.v3i2.4333
Qohar, A. L., & Suharjito. (2022). Smart agriculture for optimizing photosynthesis using internet of things and fuzzy logic. International Journal of Electrical and Computer Engineering, 12(5), 5467–5480. https://doi.org/10.11591/ijece.v12i5.pp5467-5480
Riahi, J., Nasri, H., Mami, A., & Vergura, S. (2024). Effectiveness of the fuzzy logic control to manage the microclimate inside a smart insulated greenhouse. Smart Cities, 7(3), 1304–1329. https://doi.org/10.3390/smartcities7030055
Saha, G., Shahrin, F., Khan, F. H., Meshkat, M. M., & Azad, A. A. M. (2025). Smart IoT-driven precision agriculture: Land mapping, crop prediction, and irrigation system. PLOS ONE, 20(3), Article e0319268. https://doi.org/10.1371/journal.pone.0319268
Sari, I. Y., & Wibowo, A. (2025). Sistem kendali suhu greenhouse berbasis Arduino dengan metode histeresis dan override kelembapan tanah. Jurnal Ilmiah MATRIK, 27(3), 233–241.
Subahi, A. F., & Bouazza, K. E. (2020). An intelligent IoT-based system design for controlling and monitoring greenhouse temperature. IEEE Access, 8, 125488–125500. https://doi.org/10.1109/ACCESS.2020.3007955
Thomopoulos, V., Tolis, F., Blounas, T.-F., Tsipianitis, D., & Kavga, A. (2024). Application of fuzzy logic and IoT in a small-scale smart greenhouse system. Smart Agricultural Technology, 8, Article 100446. https://doi.org/10.1016/j.atech.2024.100446
Tian, J., Li, D., & Jia, X. (2022). IoT smart agriculture and agricultural product income insurance participant behavior based on fuzzy neural network. Computational Intelligence and Neuroscience, 2022, Article 4778975. https://doi.org/10.1155/2022/4778975
Wardhani, R. N., & Shafira, A. A. (2021). Pemodelan simulasi sistem kontrol temperatur berbasis fuzzy logic pada automatic greenhouse. Dalam Seminar Nasional Teknik Elektro (hlm. 208–215).