MACHINE LEARNING CLASSIFICATION OF JUVENILE AND YOUTHFUL OFFENDERS USING SOCIO-GEOGRAPHIC CRIME INDICATORS FROM MALAYSIAN ADMINISTRATIVE DATA | Zenodo Skip to main Communities My dashboard Log in Sign up Journal of Theoretical and Applied Information Technology Published April 30, 2026 | Version v1 Journal article Open MACHINE LEARNING CLASSIFICATION OF JUVENILE AND YOUTHFUL OFFENDERS USING SOCIO-GEOGRAPHIC CRIME INDICATORS FROM MALAYSIAN ADMINISTRATIVE DATA Authors/Creators NURAZEAN MAAROP , GANTHAN NARAYANA SAMY , ROSLINA MOHAMMAD , NORZIHA MEGA MOHD ZAINUDDIN , WAN ROSANISAH WAN MOHD Description 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. Files 2Vol104No8.pdf Files (1.1 MB) Name Size Download all 2Vol104No8.pdf md5:b94899903bad3a0bb4e0dc049d07fc55 1.1 MB Preview Download 35 Views 11 Downloads Show more details All versions This version Views Total views 35 35 Downloads Total downloads 11 11 Data volume Total data volume 13.9 MB 13.9 MB More info on how stats are collected.... Versions External resources Indexed in OpenAIRE Communities Keywords and subjects Keywords Machine Learning, Crime Analytics, Juvenile Offenders, Random Forest, Socio-Geographic Indicators, Data Mining. Details DOI DOI Badge DOI 10.5281/zenodo.19971876 Markdown [](https://doi.org/10.5281/zenodo.19971876) reStructuredText .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.19971876.svg :target: https://doi.org/10.5281/zenodo.19971876 HTML <a href="https://doi.org/10.5281/zenodo.19971876"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.19971876.svg" alt="DOI"></a> Image URL https://zenodo.org/badge/DOI/10.5281/zenodo.19971876.svg Target URL https://doi.org/10.5281/zenodo.19971876 Resource type Journal article Publisher Little Lion Scientific Published in Journal of Theoretical and Applied Information Technology, 104(8), ISSN: 1992-8645, 2026. Rights License Creative Commons Attribution 4.0 International The Creative Commons Attribution license allows re-distribution and re-use of a licensed work on the condition that the creator is appropriately credited. Read more Copyright Little Lion Scientific Citation Export Technical metadata Created May 2, 2026 Modified May 2, 2026 Jump up About About Policies Infrastructure Principles Projects Roadmap Contact Blog Blog Support Help FAQ Developers REST API OAI-PMH Contribute GitHub Donate Funded by Powered by CERN Data Centre & InvenioRDM Status Privacy policy Cookie policy Terms of Use This site uses cookies. Find out more on how we use cookies Accept all cookies Accept only essential cookies