AULIA MAHARANI, NPM 2107100001 (2024) IMPLEMENTATION OF THE K-MEANS CLUSTERING METHOD IN CLUSTERING POOR POPULATION IN BANDAR KUMBUL VILLAGE, LABUHANBATU REGENCY. Tugas_Akhir (Artikel) International Journal of Science, Technology & Management, 5 (4). pp. 1015-1023. ISSN 2722-4015 (e-ISSN)
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Abstract
Poverty is one of the crucial social problems in rural areas. The problem of poverty in rural areas is increasingly in the spotlight because of its broad impact on the community's economic and social sustainability. The varying levels of poverty require an appropriate analytical approach to design effective intervention programs. In an effort to understand and address this problem, this study uses the K-Means Clustering method to group the poor population. We use KMeans clustering to identify and group hamlets based on their poverty levels. This study aims to categorize the hamlets in Bandar Kumbul Village into multiple clusters according to their poverty levels, thereby identifying which hamlets necessitate more focused attention. The research methods used include collecting data on the number of poor people from 2013 to 2022 in each hamlet, data preprocessing, applying the Elbow method to determine the optimal number of clusters, and applying the K-Means Clustering algorithm to group the hamlets. The results of the study show that there are three main clusters with different characteristics. Cluster 0 includes Hutaimbaru and Mailil Julu hamlets with high poverty levels. Cluster 1 only includes the Pasir Sidimpuan hamlet, which has medium poverty levels. Cluster 2 includes Aek Mardomu, Bandar Kumbul, Mailil Jae, Sidodadi, and Singga Mata hamlets with low poverty levels. Variations in distance from the cluster center indicate significant differences in the distribution of poverty in each hamlet. The KMeans Clustering method is effective in identifying and grouping hamlets based on poverty levels, providing useful insights for the government and stakeholders to design more targeted intervention programs. Clusters with high poverty levels require immediate intervention, while clusters with medium and low poverty levels require maintenance and support to prevent an increase in poverty. This study provides a strong foundation for decision-making and policies to reduce poverty levels in Bandar Kumbul Village more effectively and sustainably. Keywords : Bandar Kumbul, Clustering, Elbow Method, K-Means, and Poverty
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Bandar Kumbul, Clustering, Elbow Method, K-Means, and Poverty. |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Fakultas Sains Dan Teknologi > Informatika Komputer |
| Depositing User: | Unnamed user with email repository@ulb.ac.id |
| Date Deposited: | 14 Apr 2025 08:00 |
| Last Modified: | 14 Apr 2025 08:00 |
| URI: | http://repository.ulb.ac.id/id/eprint/1258 |
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