Application of Artificial Neural Network (MLP) for Multivariate Analysis of Stunting Causes in Indonesia
DOI:
https://doi.org/10.30871/jaic.v9i4.10205Keywords:
Food Security Index, Multi-Layer Perceptron (MLP), StuntingAbstract
Stunting is a major public health challenge in Indonesia, primarily caused by prolonged malnutrition and recurrent infections during the First 1,000 Days of Life. This study utilizes the Multi-Layer Perceptron (MLP) neural network model to predict stunting, offering a new dimension in the analysis of complex data and identification of patterns influencing stunting. With its capabilities, the MLP model provides higher precision in detecting contributing factors to stunting. The evaluation results of the model show RMSE of 0.7231, MAE of 3.0313, and an R² value of 0.9463. The Food Security Index (IKP), feature X9, had the highest feature importance, followed by X5 (Lack of Clean Water) and X1 (NCPR). This study presents a novel approach to predicting stunting percentages and offers more objective insights to support evidence-based and effective health policies aimed at reducing stunting prevalence in Indonesia.
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