PENGENDALIAN PENCEMARAN AIR BERBASIS IOT MENGGUNAKAN ALGORITMA NAIVE BAYES PADA SUNGAI CILIWUNG KALIMALANG JAKARTA TIMUR
DOI:
https://doi.org/10.31539/rexwen75Abstract
Penelitian ini mengembangkan sistem monitoring kualitas air berbasis Internet of Things (IoT) di sungai Cisadane dan Ciliwung yg mengalir diwilayah Kota Bogor. Sistem ini menggunakan mikrokontrolerArduino ESP32 dan platform Blynk untuk memantau parameter suhu, pH, Total Dissolved Solids (TDS) secara real-time yang kemudian diolah hasilnya menggunakan algoritma Naives Bayes. Sensor-sensor dikalibrasi untuk memastikan akurasi dan evaluasi kinerja menggunakan Mean Absolute Percentage Error menunjukkan hasil baik, khususnya pada sensor pH dan TDS. Pengujian lapangan dilakukan pada kondisi cuaca hujan dan panas untuk menilai stabilitas pengukuran. Hasil menunjukkan nilai TDS lebih tinggi pada kondisi panas, pH lebih tinggi saat hujan, sedangkan suhu relatif stabil. Sistem ini memungkinkan masyarakat memantau kualitas air secara mandiri melalui aplikasi, serta mendukung pengelolaan sumber daya air yang berkelanjutan. Implementasi ini menunjukkan potensi teknologi IoT dalam meningkatkan kesadaran dan partisipasi masyarakat terhadap kualitas lingkungan lokal.
Kata Kunci: IoT, ESP32, Kualitas Air Limbah, TDS, pH, Blynk Naive Bayes, Monitoring Real-time, Klasifikasi Air.
References
[1] H. A. Afan et al., “Data-driven water quality prediction for wastewater treatment plants,” Heliyon, vol. 10, no. 18, Sep. 2024, doi: 10.1016/j.heliyon.2024.e36940.
[2] M. Flores-Iwasaki, G. A. Guadalupe, M. Pachas-Caycho, S. Chapa-Gonza, R. C. Mori-Zabarburú, and J. C. Guerrero-Abad, “Internet of Things (IoT) Sensors for Water Quality Monitoring in Aquaculture Systems: A Systematic Review and Bibliometric Analysis,” Mar. 01, 2025, Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/agriengineering7030078.
[3] A. C. P. Fernandes, A. R. Fonseca, F. A. L. Pacheco, and L. F. S. Fernandes, “MethodsX Water quality predictions through linear regression - A brute force algorithm approach,” vol. 10, no. March, 2023, doi: 10.1016/j.mex.2023.102153.
[4] V. No, T. Zubaidah, S. Hamzani, and A. C. Legowo, “Al-Ard : Jurnal Teknik Lingkungan Transforming River Water Quality Monitoring : An Advanced IoT and,” vol. 10, no. 1, pp. 31–38, 2024.
[5] M. G. Uddin, S. Nash, A. Rahman, and A. I. Olbert, “Performance analysis of the water quality index model for predicting water state using machine learning techniques,” Process Saf. Environ. Prot., vol. 169, pp. 808–828, Jan. 2023, doi: 10.1016/j.psep.2022.11.073.
[6] A. Marwa, N. #1, N. Anggis Suwastika, and R. Yasirandi, “Prediksi Kondisi Pencemaran Air Sungai Citarum Berbasis Internet of Things dan Klasifikasi Naive Bayes,” Ind. J. Comput., Mar. 2020, doi: 10.21108/indojc.2020.5.1.317.
[7] Guo, H., Jeong, K., Lim, J., Jo, J., Kim, Y. M., Park, J., Kim, J. H., & Cho, K. H. (2015). Prediction of effluent concentration in a wastewater treatment plant using machine learning models. Journal of Environmental Sciences, 32, 90–101. https://doi.org/10.1016/j.jes.2015.01. 007
[8] V. Sangwan and R. Bhardwaj, “Machine learning framework for predicting water quality classi fi cation,” vol. 19, no. 11, pp. 4499–4521, 2024, doi: 10.2166/wpt.2024.259.
[9] J. Teknik Lingkungan, T. Zubaidah, S. Hamzani, and A. Cahyo Legowo, “Al-Ard: Jurnal Teknik Lingkungan Transforming River Water Quality Monitoring: An Advanced IoT and Sensor-Based”, [Online]. Available: http://jurnalsaintek.uinsa.ac.id/index.php/alard/index
[10] T. M. Mitchell, Machine Learning. McGraw-Hill Science/Engineering/Math, 1997.
[11] A. G. Widowati, N. A. Suwastika, and R. Yasirandi, “Deteksi Lokasi Pencemaran Air Sungai Citarum berbasis IoT menggunakan Fuzzy Inference System,” vol. 4, pp. 1–14, 2019, doi: 10.21108/indojc.2019.4.3.315.
[12] K. P. Murphy, A Probabilistic Perspective, June, 2012. Cambridge, Massachusetts, London, England: The MIT Press, 2012.
[13] I. Kabir et al., “Production and Evaluation of Quality Characteristics of Edible Fish Powder from Tilapia ( Oreochromis mossambicus ) and Silver Carp ( Hypophthalmichthys molitrix ),” vol. 2022, 2022, doi: 10.1155/2022/2530533.
[14] P. Boccadoro, I. Student, V. Daniele, P. Di Gennaro, and D. L. Ieee, “Water Quality Prediction on a Sigfox-compliant IoT Device : The Road Ahead of WaterS,” pp. 1–13.
[15] H. Fakhrurroja, E. Triono, and A. Munandar, “Journal of Mechatronics , Electrical Power , Water quality assessment monitoring system using fuzzy logic and the internet of things,” vol. 14, no. 2, pp. 198–207, 2023.
[16] H. M. Forhad et al., “IoT based real-time water quality monitoring system in water treatment plants (WTPs),” Heliyon, vol. 10, no. 23, Dec. 2024, doi: 10.1016/j.heliyon.2024.e40746.
[17] A. M. Joshi, S. Prabhune, and D. J. B. N, “Sarcasm Detection using Naïve Bayes SVM Hybrid,” no. 3, pp. 1138–1142, 2019, doi: 10.35940/ijrte.C4258.098319.
[18] P. Jayaraman, K. Krishnan, and P. Partheeban, “International Journal of Information Critical review on water quality analysis using IoT and machine learning models,” Int. J. Inf. Manag. Data Insights, vol. 4, no. 1, p. 100210, 2024, doi: 10.1016/j.jjimei.2023.100210.
[19] S. Hwang, J. Park, W. Lee, and Y. Lee, “Evaluation method for probability distribution and prediction models for chlorophyll-a based on statistical models and arti cial intelligence-based algorithms”.
[20] Cloete, N. A., Malekian, R., & Nair, L. (2016). Design of Smart Sensors for Real-Time Water Quality Monitoring. IEEE Access, 4, 3975–3990. IEEE Access. https://doi.org/10.1109/ACCESS.201 6.2592958
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Dadang Iskandar Mulyana, Mesra Betty Yel, Alaqsha Gilang Imantara

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

