Clustering Indonesian Provinces Based on Poverty Levels Utilizing the Average Linkage Method with Principal Component Analysis

DOI

10.2991/978-94-6463-520-1_13How to use a DOI?

Keywords

Poverty; Clustering; Average Linkage; Principal Component Analysis

Abstract

Indonesia grapples with the pervasive issue of poverty that undermines the well-being of its citizens. Recognizing the diverse characteristics of each region in Indonesia, effective poverty alleviation policies must be tailored through a nuanced approach. Therefore, this research is crucial as it aims to employ advanced clustering techniques, specifically the Average Linkage Method with Principal Component Analysis, to discern the characteristics of poverty across Indonesian provinces. Hierarchical cluster analysis with average linkage is deemed more stable. In this cluster analysis, two assumptions must be met: the assumption of sample adequacy and multicollinearity. In cases of multicollinearity violations, Principal Component Analysis is applied for resolution. This research utilizes secondary data from the Central Statistics Agency (BPS), examining factors such as the percentage of impoverished people, poverty depth and severity indices, human development index, and average and expected length of schooling to assess poverty levels. From the research results, 2 clusters were obtained. The first cluster has low poverty levels, consisting of 31 provinces excluding Papua, West Papua and East Nusa Tenggara. Those three provinces are classified as cluster 2, with high poverty levels. This research offers vital insights for policymakers, facilitating targeted policies aligned with SDGs for effective poverty reduction strategies.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Paskal Immanuel Kontoro
AU  - Junaidi Junaidi
AU  - Nurul Fiskia Gamayanti
AU  - Arditya Sulistya Ningsih Apusing
PY  - 2024
DA  - 2024/12/05
TI  - Clustering Indonesian Provinces Based on Poverty Levels Utilizing the Average Linkage Method with Principal Component Analysis
BT  - Proceedings of the 5th International Seminar on Science and Technology (ISST 2023)
PB  - Atlantis Press
SP  - 78
EP  - 86
SN  - 2352-541X
UR  - https://doi.org/10.2991/978-94-6463-520-1_13
DO  - 10.2991/978-94-6463-520-1_13
ID  - Kontoro2024
ER  -