Comparations of Supervised Machine Learning Techniques in Predicting the Classification of the Household’s Welfare Status

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Diterbitkan: Apr 23, 2019
DOI: https://doi.org/10.30818/jpkm.2019.2040105
Kata Kunci:
classification, data training and testing, k-fold cross-validation, integrated database, supervised machine learning
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Poverty has been a major problem for most countries around the world, including Indonesia. One approach to eradicate poverty is through equitable distribution of social assistance for target households based on Integrated Database of social assistance. This study has compared several well-known supervised machine learning techniques, namely: Naïve Bayes Classifier, Support Vector Machines, K-Nearest Neighbor Classification, C4.5 Algorithm, and Random Forest Algorithm to predict household welfare status classification by using an Integrated Database as a study case. The main objective of this study was to choose the best-supervised machine learning approach in predicting the classification of household’s welfare status based on attributes in the Integrated Database. The results showed that the Random Forest Algorithm was the best.

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Vol 4 No 1 (2019): April 2019
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