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CART Classification on Ordinal Scale Data with Unbalanced Proportions using Ensemble Bagging Approach | Arini | JTAM (Jurnal Teori dan Aplikasi Matematika)

CART Classification on Ordinal Scale Data with Unbalanced Proportions using Ensemble Bagging Approach

Luthfia Hanun Yuli Arini, Solimun Solimun, Achmad Efendi, Mohammad Ohid Ullah

Abstract


CART is one of the algorithms in data exploration techniques with decision tree techniques. Unbalanced class proportions in the classification process can cause classification results of minor data to be incorrect. One way to overcome the problem of data imbalance is to use an ensemble bagging algorithm. The bagging algorithm utilizes the resampling method to carry out classification so that it can reduce bias in imbalanced data. The data used is secondary data from Fernandes and Solimun's 2023 research report. The number of sample are 100 respondents that has been valid and reliable. The sample for this research was mothers with toddlers in Wajak village, Malang Regency. The results showed that the ensemble bagging CART method is better at overcoming the problem of imbalance in the proportion of classes with a performance value of accuracy, sensitivity, specificity, and F1-Score values of 85%, 94.1%, 66.7%, and 78%. This research is limited to the Sumberputih Village area. So, the results of this research are only representative for the Wajak District area.

 


Keywords


Unbalanced; CART; Bagging.

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DOI: https://doi.org/10.31764/jtam.v8i2.20201

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