This study compares four classification models—Logistic Regression, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost)—to predict women's labor force participation in West Java, using a dataset of 62 features. After feature selection, the dataset was reduced to 31 features, followed by modeling with the top 10 most important features from each model. Model performance, evaluated using Balanced Accuracy, F1-Score, and Cohen’s Kappa, showed similar results, with RF and XGBoost slightly outperforming the others. However, the differences were not significant, indicating comparable predictive ability across models. The top 10 features from each model were averaged, and the five most influential features were selected. Key factors influencing women's employment status include household responsibilities, age, education, district minimum wage, and the age of the youngest child. The analysis found that 79.6% of unemployed women manage household duties, while employed women are less involved (18.9%). Age was significant, with employed women mostly in the 35-55 age range, correlating with older children and greater workforce participation. Additionally, employed women are more likely to come from regions with lower minimum wages, suggesting that economic necessity drives their labor market participation.
Keywords: female labor force, machine learning, classification, West Java
