DOI : https://doi.org/10.18517/ijaseit.15.2.20339

Development of an Internet of Things-Based Water Quality Monitoring System for Shrimp Ponds Utilizing Mappi32

Herlinawati (1), Afri Yudamson (2), Sri Ratna Sulistiyanti (3), Muhammad Ifan Saputra (4), Bagus Hendrawan (5)
(1) Department of Electrical Engineering, University of Lampung, Lampung, Indonesia
(2) Department of Electrical Engineering, University of Lampung, Lampung, Indonesia
(3) Department of Electrical Engineering, University of Lampung, Lampung, Indonesia
(4) Department of Electrical Engineering, University of Lampung, Lampung, Indonesia
(5) Department of Electrical Engineering, University of Lampung, Lampung, Indonesia
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Herlinawati, A. Yudamson, S. R. Sulistiyanti, M. I. Saputra, and B. Hendrawan, “Development of an Internet of Things-Based Water Quality Monitoring System for Shrimp Ponds Utilizing Mappi32”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 15, no. 2, pp. 426–435, Apr. 2025.
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Indonesia's vast waters make its aquatic resources abundant, with one of the most valuable resources being Vannamei shrimp. Technology-based shrimp farming is an essential method for utilizing this resource. Maintaining optimal water quality parameters is necessary for successful shrimp farming, as it affects shrimp breeding. Ideal conditions for shrimp breeding include a temperature range of 26-32°C, salinity of 30-35 ppt, pH of 7.6-8.3, turbidity of 11-24 NTU, and oxygen levels of more than 4 mg/L. This research integrates sensors for real-time shrimp pond water quality monitoring using Internet of Things (IoT) technology, with sensors calibrated for error rate and accuracy.  The error rates of the sensor testing process are TDS sensor 5.39%, temperature sensor 0.14%, pH sensor 2.85%, turbidity sensor 5.14%, and DO sensor 8.84%. This research successfully monitored the water quality of shrimp ponds in real-time through the Node-Red dashboard with the MQTT IoT protocol and an average latency of 593.3 ms. Over ten days, the average water quality parameters were as follows: in the morning salinity 31.86 ppt, temperature 27.13°C, pH 7.66, turbidity 17.24 NTU, and dissolved oxygen 5.22 mg/L; in the afternoon salinity 31.69 ppt, temperature 27.41°C, pH 7.84, turbidity 17.24 NTU, and dissolved oxygen 5.54 mg/L; and at night salinity 31.90 ppt, temperature 27.35°C, pH 7.78, turbidity 17.21 NTU, and dissolved oxygen 5.36 mg/L. Further research is needed to implement the Fuzzy Logic method to determine the optimal status of shrimp pond water and evaluate the effectiveness of the process on the control system.

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