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K-means clustering analysis and multiple linear regression model on household income in Malaysia

IAES International Journal of Artificial Intelligence · Vol. 12 No. 2 (2023) · hlm. 731–738

DOI
10.11591/ijai.v12.i2.pp731-738
ISSN
2252-8938, 2089-4872
Terbit
2023-06-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Household income plays a significant role in determining a country's socioeconomic standing. This measure is often used by the government to formulate the federal budget and policies that are most appropriate for national development. In spite of this, Malaysia's current economic circumstances continue to be characterized by income disparity. Therefore, this shortcoming can be addressed by analyzing the household income survey (HIS) conducted by Department of Statistics Malaysia (DoSM). In this study, the hybrid model is proposed where K-means and multiple linear regression (MLR) for clustering and predicting household income in Malaysia. Based on the experimental results, the K-means clustering analysis in conjunction with the MLR model outperformed the MLR model without clustering with a smaller mean square error. As a result, clustering analysis results in a more accurate estimate of household income because it reduces the variation between households. It is important that household income information reflect the concern of policymakers about the impact of universal and targeted interventions on different socioeconomic groups.

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  • Tun Hussein Onn University of Malaysia
  • Universitas Sembilanbelas November Kolaka
  • Hasanuddin University
  • Universitas Ahmad Dahlan
  • Sultan Idris Education University
  • Universitas Jambi
  • Universitas Islam Negeri Alauddin Makassar
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Rekan penulisOrganisasiArtikel bersama
Nurfarawahida RamlyTun Hussein Onn University of Malaysia4
Özlem Gürünlü AlmaMuğla University4
Aida MustaphaTun Hussein Onn University of Malaysia3
Nursalam NursalamUniversitas Sembilanbelas November Kolaka2
Kartomo KartomoUniversitas Sembilanbelas November Kolaka2
Muhammad Obi KasminUniversitas Sembilanbelas November Kolaka2
Fitriani Nur DamayantiUniversitas Muhammadiyah Semarang2
Harapin HafidUniversitas Halu Oleo1
La Ode BaaAfiliasi tidak diketahui1
Sakaria SakariaHasanuddin University1
Muhammad BasrunHasanuddin University1
Surianti SuriantiHasanuddin University1
Nirwana IndahHasanuddin University1
Muhtar AminUniversitas Sembilanbelas November Kolaka1
Mhd. Usni Zamzami HasibuanUniversitas Jambi1
Roli MardianUniversitas Jambi1
Bella AulinaAfiliasi tidak diketahui1
Bambang IsmantoAfiliasi tidak diketahui1
Robbi TanggaraAfiliasi tidak diketahui1
Sakaruddin MandjarrekiUniversitas Islam Negeri Alauddin Makassar1
Muhammad Alim IhsanUniversitas Islam Negeri Alauddin Makassar1
Ismanto IsmantoSunan Gunung Djati State Islamic University Bandung1
Rizki Firmansyah Setya BudiNational Nuclear Energy Agency of Indonesia1
Shazlyn Milleana ShaharudinSultan Idris Education University1
Siti Mariana Che Mat NorSultan Idris Education University1
Norhaiza AhmadUniversity of Technology Malaysia1
Wan Nur Hawa Fatihah Wan ZureyTun Hussein Onn University of Malaysia1
Nazim RazaliTun Hussein Onn University of Malaysia1
Suhaila Mohd. YasinTun Hussein Onn University of Malaysia1
Mazwin Arleena MasngutTun Hussein Onn University of Malaysia1
Anggit PrabowoUniversitas Ahmad Dahlan1
Chong Su LiUniversiti Teknologi Petronas1
Dahlia JananSultan Idris Education University1
TRI DESSY DAMAYANTIUniversitas Ahmad Dahlan1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">K-means clustering analysis and multiple linear regression model on household income in Malaysia</dc:title>
	<dc:creator>Yee, Gan Pei</dc:creator>
	<dc:creator>Rusiman, Mohd Saifullah</dc:creator>
	<dc:creator>Ismail, Shuhaida</dc:creator>
	<dc:creator>Suparman, Suparman</dc:creator>
	<dc:creator>Hamzah, Firdaus Mohamad</dc:creator>
	<dc:creator>Shafi, Muhammad Ammar</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Household income; K-means clustering; Mean square error; Multiple linear regression; Silhouette analysis</dc:subject>
	<dc:description xml:lang="en-US">Household income plays a significant role in determining a country's socioeconomic standing. This measure is often used by the government to formulate the federal budget and policies that are most appropriate for national development. In spite of this, Malaysia's current economic circumstances continue to be characterized by income disparity. Therefore, this shortcoming can be addressed by analyzing the household income survey (HIS) conducted by Department of Statistics Malaysia (DoSM). In this study, the hybrid model is proposed where K-means and multiple linear regression (MLR) for clustering and predicting household income in Malaysia. Based on the experimental results, the K-means clustering analysis in conjunction with the MLR model outperformed the MLR model without clustering with a smaller mean square error. As a result, clustering analysis results in a more accurate estimate of household income because it reduces the variation between households. It is important that household income information reflect the concern of policymakers about the impact of universal and targeted interventions on different socioeconomic groups.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-06-01</dc:date>
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	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/22351</dc:identifier>
	<dc:identifier>10.11591/ijai.v12.i2.pp731-738</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 12, No 2: June 2023; 731-738</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v12.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/22351/13668</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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