PREDIKSI SELF-HARM PADA REMAJA BERDASARKAN SOSIODEMOGRAFI DAN KECEMASAN MENGGUNAKAN STACKING-GENETIC ALGORITHM
Keywords:
self-harm, adolescents, anxiety, sociodemographics, stacking-genetic algorithmAbstract
Self-harm is a critical mental health issue among adolescents, often associated with emotional distress such as anxiety. This study aims to predict self-harm behavior in adolescents using a combination of sociodemographic factors and anxiety levels through a Stacking-Genetic Algorithm (Stacking-GA) model. The input variables include age, gender, parental income, family status, and anxiety categories. Feature selection was optimized using Genetic Algorithms to identify the most relevant predictors. The predictive model was developed using Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression (LR), integrated into a stacking ensemble. Evaluation metrics included accuracy, precision, recall, F1-score, MSE, and RMSE. Results showed that anxiety had a strong correlation with self-harm, while sociodemographic variables had weaker but contextually significant interactions. The Stacking-GA model outperformed the traditional stacking method, achieving an accuracy of 95%, indicating its robustness in self-harm prediction. This study confirms anxiety as a primary predictor of self-harm, with sociodemographic variables acting as contributing contextual factors. The proposed Stacking-GA approach has potential for early digital-based detection tools, supporting timely intervention and mental health services for adolescents. It also aligns with national strategies to enhance technological innovation in adolescent mental health care.
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