Prediction of Extreme Poverty Levels Using the Performance of the Multiple Linear Regression Method

B Borianto; Y Yuhandri; Rini Sovia

doi:10.35134/komtekinfo.v12i3.655

Authors

B Borianto Universitas Putra Indonesia YPTK
Y Yuhandri Universitas Putra Indonesia YPTK
Rini Sovia Universitas Putra Indonesia YPTK

DOI:

https://doi.org/10.35134/komtekinfo.v12i3.655

Keywords:

extreme poverty, prediction, MLR, socioeconomic policy, predictive model evaluation

Abstract

Extreme poverty is a type of poverty that is defined as a condition that cannot meet basic human needs. The Government of Indonesia through Presidential Instruction No. 4 of 2022 sets a target for the elimination of extreme poverty, but this effort requires an accurate and comprehensive data-driven approach. This study aims to build a model for predicting extreme poverty levels. The method used in this study is Multiple Linear Regression (MLR), which is able to measure the contribution of each predictor variable to the phenomenon of extreme poverty. The dataset processed in this study was sourced from the Dumai City Social and Community Empowerment Office. The dataset consisted of 2,007 extreme poverty data with predictor variables in the form of residence ownership (X1), employment (X2), income (X3), education (X4), and health insurance (X5). The results of this study show that the Multiple Linear Regression method is able to provide accurate predictions of the extreme poverty level in Dumai City with an accuracy rate of 87%. The model evaluation was carried out using three metrics based on the results of the test obtained R = 0.674 and R² = 0.454, which means that 45.4% of the variation in poverty status can be explained by the variables of home ownership, type of occupation, amount of income, education level, and health insurance. The ANOVA test showed a value of F = 332.777 with a significance of < 0.001, so the model was simultaneously significant. The regression coefficient showed that all variables had a negative and significant influence (p < 0.05) on poverty status, with the greatest influence coming from the type of job (β = -0.304) and amount of income (β = -0.291), followed by home ownership, health insurance, and education level. Thus, the Multiple Linear Regression method has proven to be effective in building an extreme poverty prediction system. This model can be a basic reference in supporting more targeted, measurable, and data-based socio-economic policy decision-making, especially in efforts to combat extreme poverty in a sustainable and systematic manner.

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