Grouping Regencies/Cities in West Sumatra Province Based on People’s Welfare Indicator Using Biplot Analysis

The level of community welfare is a crucial reflection of the success of development in a region. Welfare is assessed based on eight aspects: poverty, employment, education, housing, consumption patterns, health, population, and other social factors. In West Sumatra Province, the level of community welfare still requires improvement across all indicators. The determination of community welfare levels can be achieved by reviewing all dimensions based on the linear relationships between districts/cities, thereby providing insights into the indicators that still need enhancement. This effort can assist the West Sumatra Provincial Government in formulating regional policies and programs for equitable distribution and improvement of community welfare across all districts/cities. The data used in this study are secondary data obtained from the West Sumatra Provincial BPS website in 2024. The grouping of districts/cities was conducted using Principal Component Analysis based on singular value decomposition biplot analysis. The analysis results formed four groups with distinct characteristics of community welfare indicators. The groups that need to be prioritized for improvement are groups 1 and 3, which exhibit low levels of community welfare. Group 2 consists of districts/cities with high community welfare characteristics in terms of population, education, and housing. Meanwhile, group 4 includes districts/cities with high community welfare characteristics regarding consumption patterns, poverty, and labor indicators.

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Publication Details

Journal
UNP Journal of Statistics and Data Science
Published
2025-08-30
DOI
https://doi.org/10.24036/ujsds/vol3-iss3/407
Primary Topic
Data Mining and Machine Learning Applications
Type
article
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article

Grouping Regencies/Cities in West Sumatra Province Based on People’s Welfare Indicator Using Biplot Analysis

Maya Ifra Shobia, Yenni Kurniawati
UNP Journal of Statistics and Data Science
Data Mining and Machine Learning Applications
article

Grouping Regencies/Cities in West Sumatra Province Based on People’s Welfare Indicator Using Biplot Analysis

Maya Ifra Shobia, Yenni Kurniawati
article en

Abstract

The level of community welfare is a crucial reflection of the success of development in a region. Welfare is assessed based on eight aspects: poverty, employment, education, housing, consumption patterns, health, population, and other social factors. In West Sumatra Province, the level of community welfare still requires improvement across all indicators. The determination of community welfare levels can be achieved by reviewing all dimensions based on the linear relationships between districts/cities, thereby providing insights into the indicators that still need enhancement. This effort can assist the West Sumatra Provincial Government in formulating regional policies and programs for equitable distribution and improvement of community welfare across all districts/cities. The data used in this study are secondary data obtained from the West Sumatra Provincial BPS website in 2024. The grouping of districts/cities was conducted using Principal Component Analysis based on singular value decomposition biplot analysis. The analysis results formed four groups with distinct characteristics of community welfare indicators. The groups that need to be prioritized for improvement are groups 1 and 3, which exhibit low levels of community welfare. Group 2 consists of districts/cities with high community welfare characteristics in terms of population, education, and housing. Meanwhile, group 4 includes districts/cities with high community welfare characteristics regarding consumption patterns, poverty, and labor indicators.

UNP Journal of Statistics and Data ScienceVol. 3(3)
Openalex Percentile: Top 64%
Data Mining and Machine Learning Applications
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Grouping Regencies/Cities in West Sumatra Province Based on People’s Welfare Indicator Using Biplot Analysis — Maya Ifra Shobia, Yenni Kurniawati · UNP Journal of Statistics and Data Science (2025) | TGRS Research Map | TGRS