- Beranda
- Komunitas
- Story
- penelitian
variabel terbaik yang berhubungan dengan emansipasi wanita p10
TS
yuliuseka
variabel terbaik yang berhubungan dengan emansipasi wanita p10
Analysis
Linear Regression: Shows the highest MSE and MAPE, indicating it does not capture the complexity of the data well.
Polynomial Regression: Better than linear regression but still not the best in capturing non-linear relationships.
Support Vector Regression (SVR): Performs moderately well but is outperformed by more sophisticated models.
Random Forest Regression: Handles non-linear interactions effectively with better accuracy.
Neural Network: Shows the best performance with the lowest MSE and MAPE, suggesting it captures the complex patterns in the data most accurately.
Predictions for 2024 and 2025
Using the Neural Network model, which has the best performance metrics, we predict the following FLFPR for the years 2024 and 2025:
Year Predicted FLFPR (%)
2024 53.8
2025 54.2
Conclusion
The analysis suggests that the FLFPR in Indonesia's manufacturing sector has benefited from various economic factors associated with GVCs. The accurate predictions by the Neural Network model imply that factors such as FDI inflows, manufacturing value addition, and exports significantly influence women's labor force participation. As the manufacturing sector integrates further into global value chains, the participation rate of women in the labor force is likely to increase, indicating positive benefits from these economic dynamics.
Linear Regression: Shows the highest MSE and MAPE, indicating it does not capture the complexity of the data well.
Polynomial Regression: Better than linear regression but still not the best in capturing non-linear relationships.
Support Vector Regression (SVR): Performs moderately well but is outperformed by more sophisticated models.
Random Forest Regression: Handles non-linear interactions effectively with better accuracy.
Neural Network: Shows the best performance with the lowest MSE and MAPE, suggesting it captures the complex patterns in the data most accurately.
Predictions for 2024 and 2025
Using the Neural Network model, which has the best performance metrics, we predict the following FLFPR for the years 2024 and 2025:
Year Predicted FLFPR (%)
2024 53.8
2025 54.2
Conclusion
The analysis suggests that the FLFPR in Indonesia's manufacturing sector has benefited from various economic factors associated with GVCs. The accurate predictions by the Neural Network model imply that factors such as FDI inflows, manufacturing value addition, and exports significantly influence women's labor force participation. As the manufacturing sector integrates further into global value chains, the participation rate of women in the labor force is likely to increase, indicating positive benefits from these economic dynamics.
bhintuni memberi reputasi
1
4
1
Komentar yang asik ya
Urutan
Terbaru
Terlama
Komentar yang asik ya
Komunitas Pilihan