A research project focusing on weather data collection through a LoRa-based weather station and applying the XGBoost algorithm for accurate prediction and analysis. I also wrote a paper and submitted it to the IEEE! You can find the article here
Published in IEEE (Under Review), 1900
Weather prediction plays a vital role in energy, transportation, and construction sectors, where accurate forecasts can significantly reduce economic losses. In Indonesia, challenges such as uneven GSM/4G coverage and the limited accuracy of satellite data hinder reliable local predictions. This paper presents the design and implementation of a LoRa-based weather station integrated with a multi-parameter sensor and machine learning prediction using the XGBoost algorithm. The proposed system collects real-time weather data through a HONDE HD-WSM-U-07 sensor, transmits data via LoRa communication, and applies XGBoost regression models trained on historical data from an Automatic Weather Station (AWS) and local sensor measurements. Experimental results show that the proposed model achieves an R² of up to 0.9 in predicting temperature, humidity, irradiation, and rainfall. The integration of LoRa communication and machine learning enables accurate and energy-efficient weather prediction for remote areas.
Published:
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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