Interpretable Tree-Based Models for Identifying Youth Not in Employment, Education, or Training - Siti Nur Azizah; Nabila Syukri; Mauizatun Hasanah; Nimas Ayu Hapsari; Bagus Sartono; Aulia Rizki Firdawanti; Budi Susetyo; Gerry Alfa Dito (Statistika, 2026)
Abstract. Youth Not in Employment, Education, or Training (NEET) is an important indicator in evaluating the extent to which Indonesia's demographic bonus can be optimally utilized. West Java, as the province with the largest number of young people and a NEET rate above the national average, is a strategic context for analysis. Using large-scale SUSENAS 2024 data with class imbalance, this study aims to: (1) compare the performance of Decision Tree, Random Forest, and XGBoost in predicting NEET status under various class imbalance handling strategies; (2) identify key features contributing to predictions using SHAP; and (3) identify NEET typologies based on feature contribution similarities. The Random Forest–Class Weight model showed the best performance with a balanced accuracy of 0.7519. SHAP analysis identified eight key predictors contributing to NEET status, namely age, highest level of education, per capita expenditure, savings account ownership, access to financial services, marital status, KIP/PIP receipt, and gender. Local SHAP-value-based clustering yielded five NEET profiles with distinct combinations of risk factors. Two clusters showed the most distinct risk patterns, each characterized by economic vulnerability and limitations due to domestic roles, making them most easily recognized by the model, while the other clusters showed more complex risk patterns. These findings indicate that NEET status is formed through various risk pathways influenced by economic, educational, and demographic factors, thus requiring the formulation of more targeted policies.