MODEL PREDIKSI STATUS NOT IN EDUCATION, EMPLOYMENT, OR TRAINING (NEET) BERDASARKAN KARAKTERISTIK INDIVIDU DAN LINGKUNGAN DI INDONESIA

This study aims to develop a predictive model for the Not in Education, Employment, or Training (NEET) status among young people in Indonesia based on individual and environmental characteristics. The research data utilizes the 2023 National Labor
Force Survey (SAKERNAS), combined with economic growth data from 34 provinces in Indonesia, with the study population consisting of young people aged 16–30 years. The analysis method used is binary logistic regression to identify the influence of variables on the probability of young people becoming NEET. Independent variables
include gender, marital status, age group, disability, educational level, residential area, economic growth, as well as the interaction between marital status and age group. The results indicate that all variables significantly influence NEET status. Young women, those aged 16–24, individuals with disabilities, those with low educational attainment, urban residents, and unmarried individuals are more likely to be NEET. Furthermore, economic growth has not been fully inclusive in absorbing the young labor force. This study concludes that the NEET phenomenon is influenced by social, economic, and structural factors; therefore, integrated policies based on education, employment, and social protection are needed to sustainably reduce NEET rates in Indonesia.