<![CDATA[This study examines burglary as a multidimensional social phenomenon shaped by structural, environmental, and psychological factors in Indonesia. A sequential exploratory mixed-method design was employed, combining machine learning analysis of provincial socioeconomic data with qualitative interviews of burglary victims. Results showed that population density and unemployment were significant positive predictors of burglary rates, while tree-based machine learning models identified population size and poverty as key contributing factors. Qualitative findings revealed fear and insecurity, environmental disorder, distrust toward institutional protection, and adaptive coping following burglary incidents. By integrating predictive modeling with victim narratives, this study provides a more comprehensive understanding of burglary and highlights the importance of addressing both socioeconomic vulnerability and victim recovery in crime prevention.]]>
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