Analisis Variabel Kemiskinan di Indonesia dengan Model Linear Regresi dan Algoritma K-Means pada Tahun 2020-2023
DOI:
https://doi.org/10.58192/profit.v4i2.3077Keywords:
Clustering, GRDP, Poverty, Regression, UnemploymentAbstract
Poverty is one of the global issues faced by many developing countries, including Indonesia. This study aims to analyze the factors influencing poverty in Indonesia and to map provinces based on their poverty levels. The data used in this study is panel data from 34 provinces in Indonesia for the period 2020–2023, sourced from the Central Bureau of Statistics. The analytical method employed is multiple linear regression using panel data to determine the impact of gross regional domestic product (GRDP), unemployment rate, and Gini ratio on poverty levels. Furthermore, the K-Means algorithm is applied to cluster provinces based on poverty levels and the variables influencing poverty. The analysis is conducted using Knime Analytics and STATA software. The findings indicate that the unemployment rate and Gini ratio have a significant impact on poverty, whereas GRDP does not significantly affect poverty levels. Based on the clustering results of poverty levels across Indonesian provinces, Papua, West Papua, and East Nusa Tenggara fall into the highest poverty level cluster.
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