Big Data Analytics for Predicting Depression Risk in Generation Z: Integrating Self-Organizing Maps and Long Short-Term Memory

Joy Nasten Sinaga; Nuraina Nuraina; Frans Mikael Sinaga; Kelvin Kelvin; Nurhayati Nurhayati

doi:10.24014/ijaidm.v8i3.38011

Big Data Analytics for Predicting Depression Risk in Generation Z: Integrating Self-Organizing Maps and Long Short-Term Memory

Joy Nasten Sinaga, Nuraina Nuraina, Frans Mikael Sinaga, Kelvin Kelvin, Nurhayati Nurhayati

Abstract

Mental health issues among Generation Z are rising, with depression being one of the most significant challenges. Leveraging the capabilities of big data analytics and artificial intelligence, this study proposes a hybrid method combining Self-Organizing Maps (SOM) and Long Short-Term Memory (LSTM) networks to predict depression risk based on behavioral data. The SOM algorithm is utilized for clustering high-dimensional input data to uncover hidden patterns, while the LSTM network is employed to capture sequential dependencies over time. Data were collected from various digital platforms, processed, and analyzed to train and validate the proposed model. Results show that the SOM-LSTM framework significantly improves the accuracy and reliability of early depression risk detection compared to conventional models. This study contributes a scalable and adaptable model for mental health prediction that can assist in timely interventions for Generation Z

Keywords

Big Data Analytics; Depression Risk Prediction; Generation Z; Long Short-Term Memory; Mental Health; Self-Organizing Maps

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DOI: http://dx.doi.org/10.24014/ijaidm.v8i3.38011

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