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variabel terbaik yang berhubungan dengan emansipasi wanita p18
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variabel terbaik yang berhubungan dengan emansipasi wanita p18
Support Vector Regression (SVR):
Literature Review: SVR can be applied to predict labor demand in different sectors of the economy, including those involved in GVCs. Research using SVR might forecast the future demand for skilled labor in manufacturing industries, providing insights into potential job opportunities for women.
Theoretical Framework: SVR aligns with theories like the Global Value Added Theory, as it helps predict how changes in labor demand within GVCs can impact women's economic participation. By forecasting labor market trends, SVR can inform policies aimed at promoting gender-inclusive economic growth.
Random Forest Regression:
Literature Review: Random forest regression can be utilized to identify the most influential predictors of women's participation in GVCs and assess their relative importance. Studies employing random forest regression might analyze a wide range of socio-economic variables to determine which factors have the strongest impact on women's employment opportunities.
Theoretical Framework: Random forest regression can support theories such as Gender and Economic Development Theory by identifying key determinants of women's economic empowerment within GVCs. By highlighting factors such as access to education, vocational training, and supportive government policies, random forest regression can inform strategies for promoting gender equality in the workforce.
Literature Review: SVR can be applied to predict labor demand in different sectors of the economy, including those involved in GVCs. Research using SVR might forecast the future demand for skilled labor in manufacturing industries, providing insights into potential job opportunities for women.
Theoretical Framework: SVR aligns with theories like the Global Value Added Theory, as it helps predict how changes in labor demand within GVCs can impact women's economic participation. By forecasting labor market trends, SVR can inform policies aimed at promoting gender-inclusive economic growth.
Random Forest Regression:
Literature Review: Random forest regression can be utilized to identify the most influential predictors of women's participation in GVCs and assess their relative importance. Studies employing random forest regression might analyze a wide range of socio-economic variables to determine which factors have the strongest impact on women's employment opportunities.
Theoretical Framework: Random forest regression can support theories such as Gender and Economic Development Theory by identifying key determinants of women's economic empowerment within GVCs. By highlighting factors such as access to education, vocational training, and supportive government policies, random forest regression can inform strategies for promoting gender equality in the workforce.
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