K-means clustering analysis and multiple linear regression model on household income in Malaysia
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 circumsta...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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2023
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/9492/ http://eprints.uthm.edu.my/9492/1/J16020_a18540f8debfd456df2dd54f8eb422f5.pdf |
| _version_ | 1848889694295687168 |
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| author | Gan Pei Yee, Gan Pei Yee Rusiman, Mohd Saifullah Ismail , Suparman, Shuhaida Suparman, Suparman Mohamad Hamzah, Firdaus Shafi, Muhammad Ammar |
| author_facet | Gan Pei Yee, Gan Pei Yee Rusiman, Mohd Saifullah Ismail , Suparman, Shuhaida Suparman, Suparman Mohamad Hamzah, Firdaus Shafi, Muhammad Ammar |
| author_sort | Gan Pei Yee, Gan Pei Yee |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T20:30:15Z |
| format | Article |
| id | uthm-9492 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:30:15Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-94922023-07-31T07:02:25Z http://eprints.uthm.edu.my/9492/ K-means clustering analysis and multiple linear regression model on household income in Malaysia Gan Pei Yee, Gan Pei Yee Rusiman, Mohd Saifullah Ismail , Suparman, Shuhaida Suparman, Suparman Mohamad Hamzah, Firdaus Shafi, Muhammad Ammar T Technology (General) 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. 2023 Article PeerReviewed text en http://eprints.uthm.edu.my/9492/1/J16020_a18540f8debfd456df2dd54f8eb422f5.pdf Gan Pei Yee, Gan Pei Yee and Rusiman, Mohd Saifullah and Ismail , Suparman, Shuhaida and Suparman, Suparman and Mohamad Hamzah, Firdaus and Shafi, Muhammad Ammar (2023) K-means clustering analysis and multiple linear regression model on household income in Malaysia. IAES International Journal of Artificial Intelligence, 12 (2). pp. 731-738. ISSN 2252-8938 https://doi.org/10.11591/ijai.v12.i2. |
| spellingShingle | T Technology (General) Gan Pei Yee, Gan Pei Yee Rusiman, Mohd Saifullah Ismail , Suparman, Shuhaida Suparman, Suparman Mohamad Hamzah, Firdaus Shafi, Muhammad Ammar K-means clustering analysis and multiple linear regression model on household income in Malaysia |
| title | K-means clustering analysis and multiple linear regression
model on household income in Malaysia |
| title_full | K-means clustering analysis and multiple linear regression
model on household income in Malaysia |
| title_fullStr | K-means clustering analysis and multiple linear regression
model on household income in Malaysia |
| title_full_unstemmed | K-means clustering analysis and multiple linear regression
model on household income in Malaysia |
| title_short | K-means clustering analysis and multiple linear regression
model on household income in Malaysia |
| title_sort | k-means clustering analysis and multiple linear regression
model on household income in malaysia |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/9492/ http://eprints.uthm.edu.my/9492/ http://eprints.uthm.edu.my/9492/1/J16020_a18540f8debfd456df2dd54f8eb422f5.pdf |