Neuroinformatics approach: Hierarchical cluster analysis of indonesian provinces based on people's welfare indicators in the realm of data science and network studies
The welfare of people has always piqued our interest, and it remains the primary goal of nations around the world in their development endeavors. To effectively drive development efforts, it is critical to understand the diverse welfare features that exist in different locations. Thus, the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Growing Science
2024-01-01
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| Series: | International Journal of Data and Network Science |
| Online Access: | http://www.growingscience.com/ijds/Vol8/ijdns_2024_16.pdf |
| _version_ | 1797201534102011904 |
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| author | Restu Arisanti Aissa Putri Pertiwi Sri Winarni Resa Septiani Pontoh |
| author_facet | Restu Arisanti Aissa Putri Pertiwi Sri Winarni Resa Septiani Pontoh |
| author_sort | Restu Arisanti |
| collection | DOAJ |
| description | The welfare of people has always piqued our interest, and it remains the primary goal of nations around the world in their development endeavors. To effectively drive development efforts, it is critical to understand the diverse welfare features that exist in different locations. Thus, the purpose of this statistical analysis is to classify Indonesian provinces based on a comprehensive set of People's Welfare Indicators, which includes Population Density (PD), Percentage of Poor Population (PPP), Life Expectancy Rate (LER), and Average Years of Schooling (AYS). The methodology used in this study is Hierarchical Cluster Analysis, which employs five distinctive techniques: Single Linkage, Average Linkage, Complete Linkage, Ward's Linkage, and the Centroid Method. The data for this study was obtained from reliable secondary sources, notably the official website of the Central Bureau of Statistics (BPS), and it provides insights on Indonesia's welfare picture in 2021. The average linkage approach shows as the most suitable of the five hierarchical cluster analysis methods used, with the closest cophenetic correlation to 1. The analysis reveals three distinctive clusters within the Indonesian context. Cluster 1 demonstrates a tendency toward low PWI (People's Welfare Index) status, while Cluster 2 exhibits a notably high PWI status. Cluster 3 occupies an intermediate position, characterized by moderate PWI status. These findings not only give useful classification but also act as an important reference point for the Indonesian government. They provide an in-depth insight into each province's distinct welfare features, supporting smart resource allocation and prioritizing aid distribution in regions of highest need. As a result, this research is an essential resource for creating equitable and effective policies and methods to improve people's well-being throughout Indonesia. |
| first_indexed | 2024-04-24T07:49:04Z |
| format | Article |
| id | doaj.art-941f127c922b44b49355b9aa4d5aec9e |
| institution | Directory Open Access Journal |
| issn | 2561-8148 2561-8156 |
| language | English |
| last_indexed | 2024-04-24T07:49:04Z |
| publishDate | 2024-01-01 |
| publisher | Growing Science |
| record_format | Article |
| series | International Journal of Data and Network Science |
| spelling | doaj.art-941f127c922b44b49355b9aa4d5aec9e2024-04-18T15:10:24ZengGrowing ScienceInternational Journal of Data and Network Science2561-81482561-81562024-01-01831969197610.5267/j.ijdns.2024.1.016Neuroinformatics approach: Hierarchical cluster analysis of indonesian provinces based on people's welfare indicators in the realm of data science and network studiesRestu ArisantiAissa Putri PertiwiSri WinarniResa Septiani Pontoh The welfare of people has always piqued our interest, and it remains the primary goal of nations around the world in their development endeavors. To effectively drive development efforts, it is critical to understand the diverse welfare features that exist in different locations. Thus, the purpose of this statistical analysis is to classify Indonesian provinces based on a comprehensive set of People's Welfare Indicators, which includes Population Density (PD), Percentage of Poor Population (PPP), Life Expectancy Rate (LER), and Average Years of Schooling (AYS). The methodology used in this study is Hierarchical Cluster Analysis, which employs five distinctive techniques: Single Linkage, Average Linkage, Complete Linkage, Ward's Linkage, and the Centroid Method. The data for this study was obtained from reliable secondary sources, notably the official website of the Central Bureau of Statistics (BPS), and it provides insights on Indonesia's welfare picture in 2021. The average linkage approach shows as the most suitable of the five hierarchical cluster analysis methods used, with the closest cophenetic correlation to 1. The analysis reveals three distinctive clusters within the Indonesian context. Cluster 1 demonstrates a tendency toward low PWI (People's Welfare Index) status, while Cluster 2 exhibits a notably high PWI status. Cluster 3 occupies an intermediate position, characterized by moderate PWI status. These findings not only give useful classification but also act as an important reference point for the Indonesian government. They provide an in-depth insight into each province's distinct welfare features, supporting smart resource allocation and prioritizing aid distribution in regions of highest need. As a result, this research is an essential resource for creating equitable and effective policies and methods to improve people's well-being throughout Indonesia.http://www.growingscience.com/ijds/Vol8/ijdns_2024_16.pdf |
| spellingShingle | Restu Arisanti Aissa Putri Pertiwi Sri Winarni Resa Septiani Pontoh Neuroinformatics approach: Hierarchical cluster analysis of indonesian provinces based on people's welfare indicators in the realm of data science and network studies International Journal of Data and Network Science |
| title | Neuroinformatics approach: Hierarchical cluster analysis of indonesian provinces based on people's welfare indicators in the realm of data science and network studies |
| title_full | Neuroinformatics approach: Hierarchical cluster analysis of indonesian provinces based on people's welfare indicators in the realm of data science and network studies |
| title_fullStr | Neuroinformatics approach: Hierarchical cluster analysis of indonesian provinces based on people's welfare indicators in the realm of data science and network studies |
| title_full_unstemmed | Neuroinformatics approach: Hierarchical cluster analysis of indonesian provinces based on people's welfare indicators in the realm of data science and network studies |
| title_short | Neuroinformatics approach: Hierarchical cluster analysis of indonesian provinces based on people's welfare indicators in the realm of data science and network studies |
| title_sort | neuroinformatics approach hierarchical cluster analysis of indonesian provinces based on people s welfare indicators in the realm of data science and network studies |
| url | http://www.growingscience.com/ijds/Vol8/ijdns_2024_16.pdf |
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