Application of Multilayer Perceptron for Estimating Relative Humidity in Cilacap, Indonesia

In recent decades, relative humidity has become a research topic that has received increasing attention due to its important role in climate change and global warming. One of the most typical issues with relative humidity is data loss due to instrument deterioration. This research attempts to apply feature selection and hyperparameter tuning methods as an approach to optimizing the reliability of the multilayer perceptron (MLP) model to predict relative humidity values designed into the MLP-CV framework. The coefficient of determination (R 2 ), root mean squared error (RMSE), and absolute error (MAE) are used to determine the model's correctness. The results showed that the MLP-CV model had better accuracy compared to the MLP model for predicting relative humidity missing values, with R 2 = 0.788, RMSE = 1.838, and MAE = 1.431.