Stunting has a significant impact on children's health and,in the long term,negatively affects productivity and GDP by 2–3%.Therefore,it is crucial to reduce stunting rates through regional mapping based on their...Stunting has a significant impact on children's health and,in the long term,negatively affects productivity and GDP by 2–3%.Therefore,it is crucial to reduce stunting rates through regional mapping based on their capacity to address stunting and by evaluating relevant indicators.A multi-criteria decision making(MCDM)approach,utilizing principal component analysis(PCA)and Entropy for weighting,and MARCOS,COPRAS,and WASPAS for ranking,can be applied.The weighting results from PCA and Entropy indicate that access to drinking water(C11)and households receiving food assistance(C10)are the largest contributing factors,while the smallest contributors are poverty rate(C7)and Gini ratio(C6).Using PCA weights across all MCDM methods,DKI Jakarta(A11)emerges as the best-performing region,while Papua(A34)ranks the worst.When Entropy weights are applied,DKI Jakarta(A11)ranks first in MARCOS and WASPAS,while South Kalimantan(A22)ranks best in COPRAS.Papua(A34),however,remains the worst performer across all methods.This study concludes that the ranking results from PCA and Entropy weighting methods are identical,showing a strong correlation.This provides policymakers with confidence in assessing each province's capacity to address stunting,highlighting that Papua(A34)demonstrates relatively poor performance in managing stunting.展开更多
文摘Stunting has a significant impact on children's health and,in the long term,negatively affects productivity and GDP by 2–3%.Therefore,it is crucial to reduce stunting rates through regional mapping based on their capacity to address stunting and by evaluating relevant indicators.A multi-criteria decision making(MCDM)approach,utilizing principal component analysis(PCA)and Entropy for weighting,and MARCOS,COPRAS,and WASPAS for ranking,can be applied.The weighting results from PCA and Entropy indicate that access to drinking water(C11)and households receiving food assistance(C10)are the largest contributing factors,while the smallest contributors are poverty rate(C7)and Gini ratio(C6).Using PCA weights across all MCDM methods,DKI Jakarta(A11)emerges as the best-performing region,while Papua(A34)ranks the worst.When Entropy weights are applied,DKI Jakarta(A11)ranks first in MARCOS and WASPAS,while South Kalimantan(A22)ranks best in COPRAS.Papua(A34),however,remains the worst performer across all methods.This study concludes that the ranking results from PCA and Entropy weighting methods are identical,showing a strong correlation.This provides policymakers with confidence in assessing each province's capacity to address stunting,highlighting that Papua(A34)demonstrates relatively poor performance in managing stunting.
