<![CDATA[Vannamei shrimp farming is an important source of livelihood and local food production for coastal communities, yet its productivity is highly sensitive to rapid fluctuations in pond water quality. In many small-scale and group-based ponds, monitoring is still conducted manually and intermittently, leading to delayed corrective actions and avoidable production losses. This community engagement program aimed to strengthen local aquaculture management in the Jaring Emas Vannamei shrimp farmer group in Sumenep Regency, Indonesia, through the implementation of an Internet of Things (IoT)-based Early Warning System (EWS) for real-time water quality monitoring. The program was carried out using a participatory and practice-based approach that actively involved the partner group throughout the process, from problem identification and solution design to system installation, practical training, and ongoing assistance. The intervention introduced three ESP32-based IoT nodes equipped with pH, temperature, and dissolved oxygen (DO) sensors, supported by an MQTT communication server and integrated web and mobile applications that provided real-time dashboards, early warning alerts, and more systematic records for feeding and harvesting activities. The evaluation examined system operability, continuity of monitoring, and changes in partners’ operational practices. The findings showed that the system operated continuously and enabled real-time monitoring through both web and mobile access. The partners were increasingly able to interpret data trends, understand alerts, and use this information to take corrective actions, such as adjusting aeration when DO levels declined. Operational record-keeping also became more systematic, while harvest data indicated improved outcomes after system adoption: yields increased from approximately 830–880 kg per pond with grade B quality to approximately 1,190–1,220 kg per pond with grade A quality. Overall, the program strengthened not only technical monitoring capacity but also more preventive, responsive, and data-informed pond management practices at the community level. Future programs should strengthen post-deployment mentoring and develop simple recommendations based on historical data to support sustained adoption and broader impact.]]>
