Pengembangan Model Random Forest Regressor untuk Prediksi Kelembaban pada Pertanian Perkotaan Berkelanjutan
Abstract
Agriculture is a sector that supports food security. Currently, agriculture faces serious challenges due to climate change, land limitations, and low technology adoption. This study aims to develop an Internet of Things (IoT)-based smart farming system integrated with artificial intelligence and run through edge computing. The prototype system is designed to collect real-time data on crop growth environments using pH, TDS, temperature, humidity, and water level sensors. The data is then processed locally using the Random Forest Regressor algorithm to determine optimal environmental conditions. Test results show that the model has very high accuracy in predicting humidity (R² = 0.99; RMSE = 0.65) and temperature (R² = 0.99; RMSE = 0.17), although there are still discrepancies in extreme conditions. The integration of IoT, AI, and edge computing has proven to improve energy efficiency, accelerate response times, and provide adaptive and affordable solutions in support of sustainable urban agriculture productivity.
Keywords: Artificial Intelligence; Random Forest Regressor; IoT; Edge Computing
Abstrak
Pertanian merupakan sektor yang mendukung ketahanan pangan, saat ini pertanian menghadapi tantangan serius akibat perubahan iklim, keterbatasan lahan, dan rendahnya adopsi teknologi. Penelitian ini bertujuan mengembangkan sistem pertanian cerdas berbasis Internet of Things (IoT) yang terintegrasi dengan kecerdasan buatan dan dijalankan melalui komputasi tepi. Prototipe sistem dirancang untuk mengumpulkan data lingkungan pertumbuhan tanaman secara real-time menggunakan sensor pH, TDS, suhu, kelembaban, dan tinggi permukaan air. Data kemudian diproses secara lokal menggunakan algoritma Random Forest Regressor untuk menentukan kondisi lingkungan optimal. Hasil pengujian menunjukkan model memiliki akurasi sangat tinggi pada prediksi kelembaban (R² = 0,99; RMSE = 0,65) dan suhu (R² = 0,99; RMSE = 0,17), meskipun masih terdapat selisih pada kondisi ekstrem. Integrasi IoT, AI, dan edge computing terbukti mampu meningkatkan efisiensi energi, mempercepat respons, serta memberikan solusi adaptif dan terjangkau dalam mendukung produktivitas pertanian perkotaan berkelanjutan.
Keywords
References
S. K. S. Durai and M. D. Shamili, “Smart farming using Machine Learning and Deep Learning techniques,” Decision Analytics Journal, vol. 3, no. 3, pp. 2–30, Jun. 2022, doi: 10.1016/j.dajour.2022.100041.
N. Mahdavi, A. Dutta, S. H. Tasnim, and S. Mahmud, “Review of machine learning techniques for energy sharing and biomass waste gasification pathways in integrating solar greenhouses into smart energy systems,” Energy and AI, vol. 20, no. 2, pp. 1–37, May 2025, doi: 10.1016/j.egyai.2025.100498.
S. G. Eladl, A. Y. Haikal, M. M. Saafan, and H. Y. ZainEldin, “A proposed plant classification framework for smart agricultural applications using UAV images and artificial intelligence techniques,” Alexandria Engineering Journal, vol. 109, pp. 466–481, Dec. 2024, doi: 10.1016/j.aej.2024.08.076.
M. Ashikuzzaman, M. S. H. Swapan, and A. U. Zaman, “Integrating urban rooftop farming into city governance in megacities: A systematic literature review,” Cities, vol. 161, no. 1, pp. 1–14, Jun. 2025, doi: 10.1016/j.cities.2025.105893.
G. Papadopoulos et al., “Stakeholders’ perspective on smart farming robotic solutions,” Smart Agricultural Technology, vol. 11, no. 100916, pp. 1–26, Aug. 2025, doi: 10.1016/j.atech.2025.100916.
A. Toku, S. Twumasi Amoah, and N. Nyabanyi N-yanbini, “Exploring the potentials of urban crop farming and the question of environmental sustainability,” City and Environment Interactions, vol. 24, no. 1, pp. 2–11, Dec. 2024, doi: 10.1016/j.cacint.2024.100167.
G. A. Pratio, S. N. Rohmah, M. A. Akbarsyah, and A. E. Supriyanto, “Praktek Smart Farming Pada Kota-Kota Di Dunia,” Jurnal Bengawan Solo Pusat Kajian Penelitian dan Pengembangan Daerah Kota Surakarta, vol. 3, no. 2, pp. 88–106, Nov. 2024, doi: 10.58684/jbs.v3i2.79.
N. Khan, T. C. Lau, and B. C. Tan, “Adoption of smart urban farming to enhance social and economic well-being of elderly: a qualitative content analysis,” Food Res, vol. 7, no. 5, pp. 114–118, Sep. 2023, doi: 10.26656/fr.2017.7(5).460.
A. Upadhyay, S. G C, Y. Zhang, C. Koparan, and X. Sun, “Development and evaluation of a machine vision and deep learning-based smart sprayer system for site-specific weed management in row crops: An edge computing approach,” J Agric Food Res, vol. 18, no. 11, pp. 2–10, Dec. 2024, doi: 10.1016/j.jafr.2024.101331.
S. Ghazal, A. Munir, and W. S. Qureshi, “Computer vision in smart agriculture and precision farming: Techniques and applications,” Sep. 01, 2024, KeAi Communications Co., Florida. doi: 10.1016/j.aiia.2024.06.004.
N. Ahmed and N. Shakoor, “Advancing agriculture through IoT, Big Data, and AI: A review of smart technologies enabling sustainability,” Mar. 01, 2025, Elsevier B.V. doi: 10.1016/j.atech.2025.100848.
S. J. Wibowo, B. Hartono, and V. Lusiana, “Sistem Kontrol Lampu Otomatis dan Semi Otomatis berbasis Internet of Things,” Jurnal Ilmiah Teknik Informatika dan Sistem Informasi, vol. 2, no. 14, pp. 856–868, Aug. 2025, Accessed: Aug. 12, 2025. [Online]. Available: https://ojs.stmik-banjarbaru.ac.id/index.php/jutisi/article/view/2361
A. Chourlias, J. Violos, and A. Leivadeas, “Virtual sensors for smart farming: An IoT- and AI-enabled approach,” Internet of Things (The Netherlands), vol. 32, p. 101611, Jul. 2025, doi: 10.1016/j.iot.2025.101611.
B. V. Balaji Prabhu, R. Shashank, B. Shreyas, and O. S. Jois Narsipura, “ARIA: Augmented Reality and Artificial Intelligence enabled mobile application for Yield and grade prediction of tomato crops,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 2693–2702. doi: 10.1016/j.procs.2024.04.254.
M. A. Zamora-Izquierdo, J. Santa, J. A. Martínez, V. Martínez, and A. F. Skarmeta, “Smart farming IoT platform based on edge and cloud computing,” Biosyst Eng, vol. 177, pp. 4–17, Jan. 2019, doi: 10.1016/j.biosystemseng.2018.10.014.
J. Wang et al., “A review of the application prospects of cloud-edge-end collaborative technology in freshwater aquaculture,” Artificial Intelligence in Agriculture, vol. 15, no. 2, pp. 232–251, Jun. 2025, doi: 10.1016/j.aiia.2025.02.008.
N. Abed, R. Murgun, A. Deldari, S. Sankarannair, and M. V. Ramesh, “IoT and AI-driven solutions for human-wildlife conflict: Advancing sustainable agriculture and biodiversity conservation,” Smart Agricultural Technology, vol. 10, Mar. 2025, doi: 10.1016/j.atech.2025.100829.
M. R. Hasan, Md. M. Rahman, F. Shahriar, S. I. Khan, K. M. Mohi Uddin, and Md. M. Hasan, “Smart Farming: Leveraging IoT and Deep Learning for Sustainable Tomato Cultivation and Pest Management,” Crop Design, p. 100079, Nov. 2024, doi: 10.1016/j.cropd.2024.100079.
J. E. Chaparro, J. E. Aedo, and F. Lumbreras Ruiz, “Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms,” J Agric Food Res, vol. 18, p. 101208, Dec. 2024, doi: 10.1016/j.jafr.2024.101208.
L. Mancipe-Castro and R. E. Gutiérrez-Carvajal, “Prediction of environment variables in precision agriculture using a sparse model as data fusion strategy,” Information Processing in Agriculture, vol. 9, no. 2, pp. 171–183, Jun. 2022, doi: 10.1016/j.inpa.2021.06.007.
U. Acharya, A. L. M. Daigh, and P. G. Oduor, “Machine Learning for Predicting Field Soil Moisture Using Soil, Crop, and Nearby Weather Station Data in the Red River Valley of the North,” Soil Syst, vol. 5, no. 4, pp. 57-66, Sep. 2021, doi: 10.3390/soilsystems5040057.
I. Ficili, M. Giacobbe, G. Tricomi, and A. Puliafito, “From Sensors to Data Intelligence: Leveraging IoT, Cloud, and Edge Computing with AI,” Sensors, vol. 25, no. 6, p. 1763, Mar. 2025, doi: 10.3390/s25061763.
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