<![CDATA[In the maritime industry, the operational efficiency of ships is highly dependent on optimal engine performance. Real-time monitoring of ship engine performance is a solution to improve efficiency and reduce the risk of unexpected damage. This research develops an Internet of Things (IoT)-based monitoring system that allows for automatic collection, analysis, and visualization of data from ship engines. The system integrates sensors to measure critical parameters such as temperature, pressure, fuel consumption, and engine vibration. The collected data is sent to the cloud for analysis using artificial intelligence algorithms to provide maintenance recommendations and performance optimization. Implementation of this system can improve fuel efficiency, reduce downtime, and extend the life of ship engines. The contribution of this study lies in the development of a predictive, real-time monitoring platform that integrates IoT and artificial intelligence to significantly enhance engine reliability and operational efficiency. The system has been validated through field testing, showing measurable improvements in fuel savings, reduced maintenance costs, and early detection of mechanical anomalies.]]>
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