<![CDATA[The operational reliability of Gas Engine Power Plants (PLTMG) depends on monitoring systems capable of providing accurate information regarding equipment conditions. However, operational data remain distributed across multiple sources, limiting the implementation of integrated predictive monitoring systems. This study aimed to analyze the requirements of an Internet of Things (IoT)-based predictive monitoring system using a Requirements Engineering approach. A descriptive qualitative method was employed through observations, interviews, and operational document analysis. The results identified six candidate requirements, which were refined into ten system requirements. Using the MoSCoW prioritization method, the requirements were classified into six Must-have (60%), three Should-have (30%), and one Could-have (10%) categories. The prioritized requirements were documented in a System Requirements Specification (SRS) and validated using a Requirement Traceability Matrix (RTM). The analysis indicated that the identified system requirements could improve data availability, monitoring capabilities, analytical capabilities, maintenance decision-making effectiveness, equipment reliability, and power plant availability. This study produced Requirements Engineering artifacts, including the SRS, RTM, conceptual architecture, and Causal Loop Model (CLM), which provide references for developing IoT-based predictive monitoring systems in PLTMG and serve as a foundation for similar studies.]]>
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