Analysis of Factors Influencing Poverty in Indonesia Using the Bootstrap Aggregating Multivariate Adaptive Regression Spline
DOI:
https://doi.org/10.30871/jaic.v10i2.12414Keywords:
Bootstrap Aggregating, MARS, Poverty, RegressionAbstract
Poverty is a multidimensional problem faced by Indonesia. Poverty is caused by various interrelated factors, such as insufficient income, unequal access to education, health services, employment opportunities, and social participation. Therefore, this study aims to analyze the factors most influential on poverty at the district/city level in Indonesia using the Bagging MARS method. This applied study begins with an analysis of relevant theories and continues with data collection. The data used are secondary data obtained from the 2024 publication of the Central Statistics Agency (BPS), comprising 13 predictor variables covering indicators of education, employment, per capita expenditure, and housing facilities. The analysis method used was Bootstrap Aggregating Multivariate Adaptive Regression Spline (Bagging MARS). This method involved building a MARS model and subsequently applying bagging resampling to improve accuracy. The MARS model was trained with 39 basis functions (BF), a maximum interaction degree (MI) of 3, and a minimum observation between knots (MO) of 3, followed by 50 bagging resamples. The best Bagging MARS model was obtained on the 42nd iteration, with a minimum Generalized Cross Validation (GCV) value of 9.207091. Of the 13 variables analyzed, the variable that had the greatest influence on poverty levels was the percentage of school enrolment among poor residents aged 7 – 12 years (X_4), with a significance level of 100%.
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