RESEARCH ARTICLE


Imbalanced Data Analysis of Adolescent Unintended Pregnancy and Pre-marital Sex using Univariate and Bivariate Random Forest



Ismaini Zain1, *, Agnes Tuti Rumiati1, Erma Oktania Permatasari1, Nilam Novita Sari1
1 Department of Statistics, Faculty of Science and Data Analytics, Sepuluh Nopember Institute of Technology, Surabaya, Indonesia


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Creative Commons License
© 2023 Zain et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Statistics, Faculty of Science and Data Analytics, Sepuluh Nopember Institute of Technology, Surabaya, Indonesia; Tel: +628123036678; E-mails: ismaini_z@statistika.its.ac.id, researchcation.id@gmail.com


Abstract

Background:

Unintended pregnancy and pre-marital sex are common in adolescents due to the lack of understanding about Adolescent Sexual and Reproductive Health (ASRH), family planning, and/or birth control.

Aims:

The purpose of this study was to classify unintended pregnancy and pre-marital sex among adolescents in East Java to find the factors behind them.

Methods:

The data used in this research included women of childbearing age based on SKAP by BKKBN in 2019. This study employed SMOTE-NC as a method for handling imbalanced data and random forest as a classification method.

Results:

The result of this study showed that the most influential variable was education and domicile for unintended pregnancy and pre-marital sex, respectively.

Conclusion:

The most influential variable in the classification of unintended pregnancy and pre-marital sex was age was education and domicile for unintended pregnancy and pre-marital sex.

Keywords: Unintended pregnancy, Pre-marital sex, Classification, Imbalanced data, Random forest, Education.