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Syaidatussalihah
Politeknik Medica Farma Husada Mataram
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Journal : EIGEN MATHEMATICS JOURNAL

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Klasifikasi Status Kemiskinan Menggunakan Algoritma Random Forest Syaidatussalihah; Abdurahim
Eigen Mathematics Journal Vol. 5 No. 1 Juni 2022
Publisher : University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/emj.v5i1.133

Abstract

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.
Co-Authors Abdurahim Abdurahim Ade Kurniawan Musparlin Halid Reny Amalia Permata Sri Rahmawati Tuhfatul Ulya