MACHINE LEARNING CLASSIFICATION OF JUVENILE AND YOUTHFUL OFFENDERS USING SOCIO-GEOGRAPHIC CRIME INDICATORS FROM MALAYSIAN ADMINISTRATIVE DATA

Examining the determinants of juvenile and youthful offending is essential for understanding youth crime patterns and supporting evidence-based prevention strategies. This study applies machine learning techniques to classify individuals into juvenile offenders (children in conflict with the law) and youthful offenders using administrative crime records obtained from Malaysia’s Department of Social Welfare (Jabatan Kebajikan Masyarakat). The dataset comprises 3,879 cases, including 2,743 children under the age of 18 and 1,136 youthful offenders aged between 18 and 21 years. The analysis incorporates demographic attributes and socio-geographic crime indicators, including gender, ethnic group, type of crime, state, state crime rate, state crime density, population percentage, and crime ratio. Four supervised machine learning algorithms, namely Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine, are employed to evaluate predictive performance. Model evaluation is conducted using accuracy, precision, recall, and F1-score, with stratified five-fold cross-validation applied to assess model robustness. The results indicate that tree-based models outperform linear models in this classification task. Decision Tree and Random Forest achieve the highest performance, with an accuracy of approximately 97.6%, while Random Forest attains a mean cross-validation accuracy of 97.86%. Feature importance and SHAP analyses reveal that crime ratio, type of crime, state, and ethnic group are the most influential predictors. Overall, the findings demonstrate that integrating demographic characteristics with socio-geographic indicators enhances the predictive classification of youth offenders. The proposed framework provides a data-driven approach for analyzing administrative crime data and offers valuable insights into regional and demographic patterns associated with youth offending.