Managing energy consumption in critical government infrastructure, such as meteorological stations, requires a balance between operational reliability and efficiency. This paper presents the development of an integrated IoT system designed to support an adaptive energy management framework at the Hang Nadim Meteorological Station. The proposed system utilizes a robust wired RS-485 network to ensure stable data transmission in high-interference environments. The architecture integrates distributed client nodes for real-time power analytics and internal climate monitoring. A core feature of this development is the synchronization of internal metrics with outdoor temperature data acquired from a local Automatic Weather Station (AWS). This integration drives an adaptive control logic via IR Blasters, which dynamically adjusts AC set-points based on real-time atmospheric conditions and occupancy schedules. Experimental results demonstrate a significant reduction in energy waste: the system achieved a maximum efficiency of 38.27% in the Observation Room (ROBS) during weekdays and a consistent 23.31% saving in the Data Room (RDATA). The implementation shows that the system effectively stabilizes energy profiles and eliminates unnecessary power spikes during non-operational periods, providing a resilient and industrial-grade solution for sustainable office energy management.
Dedi Harianto Panjaitan, Eko Setijadi, P. H. Mukti· International Seminar on Int...· 0 citations
Conventional optimization still poses a significant challenge when it comes to producing the designs required for modern communication technologies. Previous studies have mainly focused on effectiveness and computational efficiency, but have not sufficiently addressed robustness, an essential factor in surrogate-assisted optimization for Microstrip Antenna (MSA) design. Unlike existing surrogate-assisted optimization approaches, this study explicitly incorporates robustness analysis, statistical validation, and sensitivity-driven interpretability into a unified multi-parameter optimization framework. This framework is referred to as a Robustness-Driven Surrogate Optimization Framework (RDSOF). A regression-based Machine Learning (ML) approach using Extreme Gradient Boosting (XGBoost) is employed to model the relationship among 11 geometric input parameters and key antenna performance metrics, including the input reflection coefficient (S₁₁), Bandwidth (BW), and Voltage Standing Wave Ratio (VSWR). The model is trained on a simulation-generated dataset comprising 1,920 samples generated from diverse geometric configurations. The surrogate model is evaluated inside the optimization loop under four optimization scenarios. Following this, robustness and sensitivity analyses are conducted to assess the reliability and influence of the design parameters. The outcomes indicate that the Differential Evolution (DE) approach achieves superior Electromagnetic (EM) performance, particularly in minimizing S₁₁. However, Particle Swarm Optimization (PSO) demonstrates greater stability, as shown by the relatively small difference in fitness standard deviation among its default and tuned configurations. Overall, the proposed RDSOF demonstrates capability in balancing exploration and exploitation while emphasizing robustness and computational efficiency for Rectangular Microstrip Antenna (RMSA) design.
Agusriandi, A. Affandi, Eko Setijadi· Engineering, Technology &...· 0 citations
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