Development of an IoT-based temperature and humidity prediction system for baby incubators using solar panels
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Abstract
Baby incubators are crucial medical devices to maintain environmental stability for babies born prematurely or have health problems. This research aims to develop a prediction system for temperature and humidity variables in baby incubators by utilizing Internet of Things (IoT) technology and solar panels as the main energy source. Despite advancements in IoT-based incubator systems, existing solutions often rely on reactive approaches, making them less effective in preventing harmful environmental fluctuations. Addressing this gap, this study focuses on optimizing temperature and humidity predictions using artificial intelligence (AI) for proactive control. Using a DHT22 sensor to measure temperature and humidity, as well as a 1 Wp solar panel, the system is designed to operate autonomously in areas with limited access to electricity. The methods used include data collection, data processing to calculate correlation coefficients, and selection of linear regression models for the analysis of relationships between variables. The results showed that the linear regression model applied had a good temperature and humidity prediction with a Mean Squared Error (MSE) value of 0.45 for the training data and 7.32 for the test data.
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