<![CDATA[Employment challenges remain a persistent issue, particularly in rapidly urbanizing regions, where job creation struggles to keep pace with the expanding labor force. While the informal sector plays a crucial role in economic absorption, its long-term sustainability and workers’ mobility within the labor market require further exploration. Existing studies have examined the determinants of employment status; however, limited research has employed advanced classification methods to analyze the characteristics influencing the transition between formal and informal employment. Addressing this gap, the present study investigates the classification of employment status (formal vs. informal) in Bandung using the Chi-Squared Automatic Interaction Detection (CHAID) method, a decision tree-based algorithm that enhances interpretability in classification analysis. This study utilizes secondary data from the Central Bureau of Statistics and applies CHAID to identify key factors influencing employment status. The findings reveal that variables such as marital status, age, and educational attainment significantly contribute to employment classification, aligning with previous research yet offering a more structured, data-driven classification framework. By employing CHAID, this study provides a clearer visual representation of the employment structure, allowing for a deeper understanding of the patterns within the labor market. The results contribute to labor economics literature by demonstrating how classification techniques can enhance workforce analysis. Furthermore, the insights gained from this study have practical implications for policymakers in designing targeted employment programs that address labor market imbalances and promote sustainable workforce development.]]>
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