<![CDATA[Youth unemployment remains a major issue in Indonesia, including in Southeast Sulawesi Province. Although the overall open unemployment rate in this province is relatively low, the unemployment rate among young people is still quite high. One contributing factor is the mismatch between educational outcomes and labor market needs, especially for those entering the workforce for the first time. In this context, vocational education is expected to enhance youth employability. Therefore, this study aims to classify youth employment status and identify the predictor that contribute most to the prediction results, particularly vocational education, using SHapley Additive exPlanations (SHAP) values to interpret model decisions. Several machine learning classification methods were evaluated, including naïve Bayes and random forest, with logistic regression used as the baseline comparison model. The findings indicate that the random forest model provides the best classification performance. Based on the analysis, vocational education and age group are the most influential predictors in classifying youth employment status in Southeast Sulawesi Province. Thus, vocational education serves as a key predictor that enhances the model’s ability to classify employment status and is associated with a higher model-predicted probability of being employed]]>
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