<![CDATA[This study successfully designed and implemented an IoT-based Maximum Power Point Tracking (MPPT) system using the Perturb and Observe (P&O) algorithm for a 100 Wp polycrystalline solar panel. The research aims to provide an integrated, cost-effective solution for optimizing solar energy extraction in Indonesia's tropical climate. The methods employed include mathematical modeling, system simulation in MATLAB/Simulink, and hardware implementation using an ESP32 microcontroller with sensors for voltage, current, irradiance, and temperature, integrated with the ThingSpeak IoT platform for real-time monitoring. System validation showed high accuracy with a Root Mean Square Error of 0.83 W. The hardware system achieved an average MPPT tracking efficiency of 95.36% with a response time of 2.05 seconds and power oscillation of 3.17%, contributing to a significant 24.8% increase in daily energy yield compared to a non-MPPT system. IoT performance metrics showed a data transmission success rate of 97.08%, average latency of 2.82 seconds, and system uptime of 99.12%. This integrated MPPT-IoT system offers a practical and affordable technological solution, contributing empirical baseline data for tropical conditions and supporting Indonesia's national energy transition and net-zero emission targets through improved data-driven decision-making.]]>
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