<![CDATA[Open unemployment classification using labor force survey data presents a significant challenge because data are highly imbalanced and consist of both numerical and categorical variables. Conventional classification models often achieve high overall accuracy but exhibit limited capability in identifying unemployed individuals, who represent the minority class. This study proposes a hybrid classification framework Fuzzy K-Prototype clustering, Synthetic Minority Over-sampling Technique for Nominal and Continuous variables (SMOTE-NC), support vector Machine, and weighted ensemble learning to improve minority-class identification in mixed-type labor force survey data. Unlike previous clustering-based ensemble approaches that primarily employ K-Means and conventional SMOTE, the proposed framework utilized Fuzzy K-Prototypes to accommodate mixed-type variables and SMOTE-NC to generate synthetic minority observations within homogenous clusters. The model was evaluated using the 2024 National Labor Survey (Sakernas) data from West Java Province, Indonesia. Experimental result indicate that proposed framework increased Recall (Sensitivity) from 73.93% to 77.94%, demonstrating an improved ability to identify unemployed individuals. Although Accuracy, Precision, Specificity, F1-score, and Balanced Accuracy decreased compared with the conventional SVM model, the proposed framework provides a more appropriate classification strategy for highly imbalance labor force datasets, where accurate identification of unemployed individuals is more important than maximizing overall classification accuracy, thereby offering valuable support for labor market analysis and employment policy development.]]>
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