Poverty Level Clustering in Districts/Cities Using the K-Medoids Method Based on Population Data

Authors

  • Cut Syahira Salsabila Universitas Malikussaleh
  • Eva Darnila Department of Informatics, Universitas Malikussaleh, Bukit Indah, Lhokseumawe, 24353, Indonesia
  • Cut Agusniar Department of Informatics, Universitas Malikussaleh, Bukit Indah, Lhokseumawe 24353, Indonesia

Keywords:

Clustering, K-Medoid, Poverty

Abstract

Poverty is a serious problem that hinders economic development, especially in developing countries like Indonesia. Aceh Province, especially Bireuen, Aceh Utara, and Lhokseumawe City have significant poverty rates due to high population and limited job opportunities. The K-Medoids algorithm used in this research works well in clustering the sub-districts in the region, with the aim of assisting the government in making more effective decisions. The implementation results show the clustering for the poverty rate in Bireuen in 2021 obtained C1 58.82%, C2 29.41%, C3 11.76%, in 2022 obtained C1 58.82%, C2 29.41%, C3 11.76%, in 2023 obtained C1 64.71%, C2 17.65%, C3 17.65%. In  Aceh Utara District, C1 62.96% was obtained, C2 33.33%, C3 3.70%, in 2022 C1 62.96%, C2 33.33%, C3 3.70%, in 2023 C1 51%, C2 44.44%, C3 3.70%. In the city of Lhokseumawe City obtained C1 25%, C2 50%, C3 25%, in 2022 C1 25%, C2 50%, C3 25%, in 2023 C1 25%, C2 25%, C3 50%. The percentage of these results shows that the poverty rate in the three regions increases every year and this requires special attention from the government to minimize the level of poverty through increasing employment, controlling the birth rate, and cash and non-cash assistance programs for poor families.

References

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Published

2024-12-27

Issue

Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)

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Articles

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Copyright (c) 2024 Cut Syahira Salsabila, Eva Darnila, Cut Agusniar

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