Kalibrasi Sensor Analog IoT Terintegrasi pH, DO, Suhu dan TDS untuk Penentuan Water Quality Index 73 25
DOI:
https://doi.org/10.26623/transformatika.v24i1.15117Keywords:
Kalibrasi Sensor Analog, ESP32, IoT, PH, DO, TDSAbstract
Water pollution is a serious environmental issue in Indonesia, necessitating an accurate and real-time water quality monitoring system. This study developed an Internet of Things (IoT)-based monitoring system using an ESP32 microcontroller integrated with four analog sensors, namely pH, Dissolved Oxygen (DO), temperature (DS18B20), and Total Dissolved Solids (TDS). The system was designed with a layered architecture comprising a sensing layer, edge processing, a communication layer based on the MQTT protocol on a Linux Ubuntu server with a Mosquitto broker, and an application and storage layer using a MySQL database and a web interface based on Laravel with real-time visualization using JavaScript and CSS. Sensor calibration was performed using a multipoint calibration approach with linear and polynomial regression, accompanied by temperature compensation and digital filtering (median filter and Exponential Moving Average). Performance evaluation was conducted through 30 simultaneous measurements against standard laboratory instruments, resulting in an average system accuracy of 90.9% with R² values ranging from 0.806 to 0.956. The TDS and temperature parameters showed the best accuracy at 94.2% and 93.3%, respectively, whereas the pH and DO achieved 86.8% and 89.3%, respectively. The results of this study indicate that the developed ESP32-based IoT system is capable of generating reliable data (accuracy above 90%) for water quality monitoring in tropical environments. This research provides a practical contribution in the form of a solution that supports the implementation of sustainable monitoring in accordance with Government Regulation Number 22 of 2021 and Sustainable Development Goal (SDG) 6.
Downloads
References
Alahi, Md. E. E., Ahsan, M. S., & Alim, M. A. (2025). IoT-based system for real-time water quality monitoring and calibration optimization. IoT, 7(2), Article 42. https://doi.org/10.3390/iot7020042
Andiyansyah, R. N., Setyawati, O., & Partiansyah, F. H. (2024). IoT-based water quality monitoring system for koi fish quarantine. Journal of EECCIS, 18(3), 92– 99.
Bok, F., Williams-Jones, A. E., & Oelkers, E. H. (2023). The solubility of oxygen in water and saline solutions. Frontiers in Nuclear Engineering, 2, Article 1158109.
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? Geoscientific Model Development, 7(1), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014
Gupta, S., Kumar, A., Sharma, R., & Singh, P. (2024). Development of a low-cost IoT- enabled multi-parameter water quality monitoring system using ESP32. Sensors, 24(5), Article 1456. https://doi.org/10.3390/s24051456
Hong, W. J., Shamsuddin, N., Abas, E., Apong, R. A., Masri, Z., Suhaimi, H., ... & Noh, M. N. A. (2021). Water quality monitoring with Arduino-based sensors. Environments, 8(1), Article 6. https://doi.org/10.3390/environments8010006
Kamaruddin, M. A., Zainal, N. A., & Abdullah, R. (2023). Calibration and performance evaluation of low-cost sensors for real-time water quality monitoring in tropical environments. Environmental Monitoring and Assessment, 195(8), Article 987. https://doi.org/10.1007/s10661-023-11567-8
Kim, S., Lee, H., Kim, J., & Lee, C. (2021). Calibration and uncertainty analysis for low- cost environmental sensors: A review. Sensors, 21(21), Article 7036. https://doi.org/10.3390/s21217036
Koritsoglou, K., Alexandris, S., Siomos, A. S., & Bartzanas, T. (2020). Improving the accuracy of low-cost sensor measurements for freezers and refrigerated vehicles. Sensors, 20(21), Article 6389. https://doi.org/10.3390/s20216389
Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017). A survey on Internet of Things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet of Things Journal, 4(5), 1125–1142. https://doi.org/10.1109/JIOT.2017.2683200
Long, F., Wang, X., & Zhang, Y. (2025). Challenges and solutions in low-cost sensor calibration for tropical water quality monitoring using ESP32. Applied Sciences, 15(6), Article 3124. https://doi.org/10.3390/app15063124
Mayton, B., Dublon, G., Russell, S., Lynch, E. F., Haddad, D. D., Ramasubramanian, V., & Paradiso, J. A. (2017). The networked sensory landscape: Capturing and experiencing ecological change across scales. Presence: Teleoperators and Virtual Environments, 26(2), 182–209. https://doi.org/10.1162/PRES_a_00278
Pemerintah Republik Indonesia. (2021). Peraturan Pemerintah Republik Indonesia Nomor 22 Tahun 2021 tentang Penyelenggaraan Perlindungan dan Pengelolaan Lingkungan Hidup. Lembaran Negara Republik Indonesia Tahun 2021 Nomor 32, Tambahan Lembaran Negara Republik Indonesia Nomor 6634.
Sari, N. I., & Rahayu, S. (2025). Status kualitas air dan beban pencemaran Sungai Brantas menggunakan metode indeks kualitas air (IKA). Jurnal Ilmu Lingkungan, 23(3), 210–225. https://doi.org/10.14710/jil.2025.23.3.210-225
Sharma, S., Mishra, D., Yadav, A., Gami, B., & Madhan, E. S. (2026). An Intelligent, low-cost water quality monitoring system with on-device machine learning and cloud integration. Scientific Reports.
Srivastava, P., Singh, V., & Patel, N. (2025). Smart water quality monitoring system using ESP32 and multi-sensor calibration techniques. Applied Sciences, 15(4), Article 2103. https://doi.org/10.3390/app15042103
United Nations. (n.d.). Goal 6: Ensure availability and sustainable management of water and sanitation for all. Sustainable Development Goals. https://sdgs.un.org/goals/goal6
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Agus Hartanto, Lenny Margaretta Huizen, Charis Maulana

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 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.

Transformatika is licensed under a Creative Commons Attribution 4.0 International License.



