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Comparative Analysis of Classification Algorithms for Citizens Welfare Status using PCA as Feature Selection

by Erfin Nur Rohma Khakim, Erik Iman Heri Ujianto
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 5
Year of Publication: 2024
Authors: Erfin Nur Rohma Khakim, Erik Iman Heri Ujianto
10.5120/ijca2024923386

Erfin Nur Rohma Khakim, Erik Iman Heri Ujianto . Comparative Analysis of Classification Algorithms for Citizens Welfare Status using PCA as Feature Selection. International Journal of Computer Applications. 186, 5 ( Jan 2024), 30-37. DOI=10.5120/ijca2024923386

@article{ 10.5120/ijca2024923386,
author = { Erfin Nur Rohma Khakim, Erik Iman Heri Ujianto },
title = { Comparative Analysis of Classification Algorithms for Citizens Welfare Status using PCA as Feature Selection },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2024 },
volume = { 186 },
number = { 5 },
month = { Jan },
year = { 2024 },
issn = { 0975-8887 },
pages = { 30-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number5/33070-2024923386/ },
doi = { 10.5120/ijca2024923386 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:29:50.464163+05:30
%A Erfin Nur Rohma Khakim
%A Erik Iman Heri Ujianto
%T Comparative Analysis of Classification Algorithms for Citizens Welfare Status using PCA as Feature Selection
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 5
%P 30-37
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The government has launched various programs to improve the welfare of citizens in order to solve the problem of poverty. The problem in poverty alleviation is on its databases. Classification of the level of welfare conventionally with the estimation method causes the classification results to be invalid. In addition, many poor people who should be the target recipients of poverty alleviation programs have yet to be recorded. This study proposes a machine learning data mining method to classify the welfare of citizens so that the results of the category of welfare levels are more computable and valid. The proposed algorithms are Naïve Bayes, Decision Tree and K-Nearest Neighbor (K-NN) and using Principal Component Analysis (PCA) as feature selection and normalization method on the preprocessing. The data that used in this research is Data Indikator Kesejahteraan Sosial (IKS). IKS data is data collected from residents of Bantul Regency in 2022. The IKS data currently consists of 95,347 rows and uses 27 attributes. There are 4 (four) class or label in this dataset include very poor, poor, nearly poor and not poor. The results of the test show that generally the best algorithm performance is K-NN with accuracy, precision and recall values respectively 96.71%, 95.16% and 88.79%. In this study, using PCA and the normalization method also had a significant effect on improving the performance of the classification algorithm. For further research, it is expected to be able to use deep learning algorithms in classifying because it has large data dimensions.

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Index Terms

Computer Science
Information Sciences

Keywords

Classification feature selection welfare poverty

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