MODELING CLUSTERWISE LINEAR REGRESSION ON POVERTY RATE IN INDONESIA - Meylisah, Eni; Rini, Dyah Setyo; Fransiska, Herlin; Agwil, Winalia; Sartono, Bagus (BAREKENG: Jurnal Ilmu Matematika dan Terapan, 2023)
When a person's income is so low that it cannot cover even the most basic living expenses, they are said to be poor. Data on poverty levels and hypothesized causes are used in this study. If the data pattern forms clusters, one of the regression analyses that can be used is Clusterwise Linear Regression (CLR). Therefore, this study aimed to determine the poverty rate modeling in Indonesia with the CLR method. The results showed that the best model is with 3 clusters, that for cluster 1, the factors that significantly affect the percentage of poverty are the percentage of electricity users , the number of small and micro industries and the number of tourist villages n cluster 2, the amount of village tours . In cluster 3, the percentage of users of electricity and the percentage of villages that have mining and quarrying .