Classification of Poverty Status using the Random Forest Algorithm
DOI:
https://doi.org/10.29303/emj.v5i1.133Keywords:
Poverty, Mean Decrease Accuracy, Random ForestAbstract
Poverty is a fundamental problem because it deals with the basic needs of society. In NTB Province, many households are living below the poverty line. One reason is that the government's efforts to reduce poverty are not optimal. Therefore, it is necessary to classify the factors that affect the poverty level so it can be used as a reference in making policies to reduce poverty. One of the classification methods is the Random Forest method. The Random Forest method with the optimal mtry and ntree scores, i.e., and , respectively, obtained an accuracy rate of 81.3%. This means that the accuracy of the Random Forest classification method for this data is very good. The income variable is the most influential factor in determining poverty status based on Random Forest analysis, with a Mean Decrease Accuracy score of 23.92%. It has the highest Mean Decrease Accuracy value among other attribute variables.References
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