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Comparison of Machine Learning Algorithms for Household’s Economic Status Classification

by Neneng Nur Sholihah, Arief Hermawan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 50
Year of Publication: 2023
Authors: Neneng Nur Sholihah, Arief Hermawan
10.5120/ijca2023923334

Neneng Nur Sholihah, Arief Hermawan . Comparison of Machine Learning Algorithms for Household’s Economic Status Classification. International Journal of Computer Applications. 185, 50 ( Dec 2023), 6-13. DOI=10.5120/ijca2023923334

@article{ 10.5120/ijca2023923334,
author = { Neneng Nur Sholihah, Arief Hermawan },
title = { Comparison of Machine Learning Algorithms for Household’s Economic Status Classification },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2023 },
volume = { 185 },
number = { 50 },
month = { Dec },
year = { 2023 },
issn = { 0975-8887 },
pages = { 6-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number50/33028-2023923334/ },
doi = { 10.5120/ijca2023923334 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:29:17.453437+05:30
%A Neneng Nur Sholihah
%A Arief Hermawan
%T Comparison of Machine Learning Algorithms for Household’s Economic Status Classification
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 50
%P 6-13
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This research addresses the global commitment to eradicate poverty as outlined in the United Nations' Sustainable Development Goals (SDGs) for 2015-2030. Poverty is a multifaceted issue encompassing income levels, resource availability, education accessibility, hunger, malnutrition, social injustice, and limited access to basic needs. Traditional poverty assessments relying on surveys present challenges in terms of cost, time, and outdatedness. To overcome these challenges, this study leverages machine learning algorithms to classify household economic status. This research compares Random Forest, SVM, Naïve Bayes, and ANN algorithms. The results show that the Random Forest algorithm consistently outperforms others, achieving the highest AUROC values. The classification evaluation results indicate that Random Forest performs the best classification with 93% accuracy. These findings contribute valuable insights for policymakers and development practitioners, enhancing the precision and efficiency of poverty reduction initiatives to align with the UN's goal of a poverty-free world by 2030.

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Index Terms

Computer Science
Information Sciences

Keywords

Comparison Classification Household Economic Status Machine Learning Algorithms.

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