Penerapan Multi-Layer Perceptron untuk Prediksi Durasi Tidur Berdasarkan Faktor Kebiasaan Harian
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Abstract
This study applies a Multi Layer Perceptron (MLP), a type of Artificial Neural Network (ANN), to predict sleep duration based on daily habits, including screen time, exercise, and caffeine intake. The methodology involves data preprocessing, MLP architecture design, hyperparameter tuning using Grid Search, and model evaluation. The final model configuration includes two hidden layers with 10 neurons each, utilizing the tanh activation function and adam optimizer with a learning rate of 0.1. The model evaluation on test data shows promising accuracy, with a Mean Squared Error (MSE) of 0.065 and Mean Absolute Error (MAE) of 0.204. These results indicate that the MLP model effectively captures complex patterns in the dataset and provides accurate sleep duration predictions. However, certain samples showed significant prediction discrepancies, suggesting the potential influence of unobserved factors, such as health conditions or stress. Further research could improve model performance by including additional features or exploring alternative models like Random Forest or Gradient Boosting.
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