<![CDATA[The phenomenon of employee resignation or turnover in Indonesia has reached a critical point that threatens operational stability and organizational competitiveness in the global market. The primary challenge faced by human resource practitioners is a reliance on static statistical models that fail to capture the temporal dimension and the evolving dynamics of risk. Conventional linear or logistic regression models often cannot accommodate censored data and may violate the proportionality assumption when applied to complex categorical variables such as profession. This study aims to model the determinants of turnover intention—including age, gender, and mode of transportation—by employing a more adaptive survival analysis approach. The main focus of the research is the application of a stratified Cox Proportional Hazards model to address violations of the Proportional Hazards assumption for the profession variable. Based on an analysis of 1,129 observations, the study identifies how turnover risk varies significantly across profession strata. We developed and compared two model configurations—with and without interaction terms—using the Akaike Information Criterion (AIC). While the non-interaction model proved most optimal for overall prediction (AIC: 5124.104), the interaction model revealed nuanced dynamics across professional strata. Key findings indicate that age generally increases turnover risk by 6.3% per year (HR: 1.063), and walking to work provides a protective effect, reducing risk by 13.6% (HR: 0.864) compared to bus usage. However, professional context significantly modulates these effects: in the 'Manage' stratum, age serves as a stabilizer (HR: 0.822), whereas male teachers face a risk 200.8% higher than their female counterparts (HR: 3.008). Furthermore, car usage in the 'Consult' stratum leads to a dramatic 423.5% increase in turnover risk (HR: 5.235). These results underscore the necessity of strata-specific retention strategies that prioritize workplace accessibility and demographic inclusivity. This study provides a robust data-driven framework for organizations to maintain workforce stability amidst the evolving labor landscape in Indonesia.]]>
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