<![CDATA[Sunshine duration (SD) is an important meteorological parameter for climate analysis and solar energy planning. In Indonesia, SD measurement still heavily relies on manual Campbell–Stokes (CS) recorders, which are prone to subjective errors, labor-intensive, and unreliable under cloudy conditions. This study develops a low-cost IoT-based SD logger using a solar cell and an INA219 sensor to estimate direct solar radiation. An ESP32 microcontroller processes the data, supported by an RTC module and an SD card for timestamping and local storage. Calibration was performed against Copernicus Atmosphere Monitoring Service (CAMS) satellite data, applying the WMO pyrheliometric threshold of 120 W/m². Remote access is provided via a Telegram bot. A 14-day field test yielded RMSE = 104.8 W/m², MAE = 56.1 W/m², and stronger correlation against CAMS (R² = 0.640, r = 0.802) than CS (r = 0.749). The mean daily SD difference was 1.09 hours against CAMS and 2.03 hours against CS. Binary classification for sunshine detection achieved an accuracy of 88.4%, precision of 79.3%, recall of 77.2%, and F1-score of 78.2%. This prototype offers an automated, accurate, weatherproof, and remotely accessible alternative to conventional CS recorders, with strong potential to advance SD monitoring and modernize meteorological infrastructure in Indonesia.]]>
Copyrights © 2026
