Analysis of Poverty Determinant Factors Based on Indonesian Province Data 2017-2023 - Nisrina Isnaini; Lukman Hakim; Mulyanto Mulyanto (Universitas KH. Abdul Chalim Mojokerto, 2025)
This study aims to identify factors contributing to poverty in Indonesia and map the distribution of poverty levels. The data used in this study are secondary data sourced from the Central Statistics Agency (BPS). This is panel data, a combination of time-series and cross-sectional data covering 34 provinces in Indonesia for 2017-2023, with multiple linear regression and selection of the best regression model using the Chow test and the Hausman test. The independent variables used are Education, GRDP, TPT, and the Human Development Index. The results show that Education, with the number of schools (Primary Schools, Junior High Schools, and Senior High Schools), has a significant positive effect; GRDP has a significant negative effect; TPT has a significant negative effect; and the Human Development Index has a significant negative effect. Mapping the distribution of poverty levels with the GeoMaps feature in the Orange Data Mining application is a visualization technique used to understand the distribution of poverty in various regions geographically. The purpose of utilizing Orange is to provide a platform for predictive models and recommendation systems.