Application of Artificial Neural Networks in Predicting the Number of Out-of-School Children Based on Expenditure Groups in Indonesia (2019–2023)
DOI:
https://doi.org/10.59934/jaiea.v5i2.2176Keywords:
Artificial Neural Network, Out-of-School Children, Household Expenditure, BPS, PredictionAbstract
Out-of-School Children (OSC) is one of the key indicators used to evaluate the equity of access to education in Indonesia. Data from Statistics Indonesia (BPS) indicate that OSC rates are influenced by differences in household expenditure groups across primary school (SD), junior high school (SMP), and senior high school (SMA) levels. This study aims to apply an Artificial Neural Network (ANN) method to predict OSC values based on historical patterns of household expenditure data. The data used in this study are secondary data from BPS covering the period 2019–2023, which are grouped into five expenditure quintiles. The ANN model employed is a Multilayer Perceptron consisting of four input neurons, two hidden layers with 24 neurons each, and one output neuron. The training process is conducted using the backpropagation algorithm with a hyperbolic tangent activation function and Min-Max Scaling for data normalization. The results indicate that the ANN model is able to consistently learn the relationship patterns between household expenditure groups and out-of-school children rates. The trained model is further used to simulate predictions of OSC values for subsequent periods. This study is expected to serve as an alternative computational approach for analyzing education indicators based on socio-economic data.
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