Data-Driven Classification of Poverty Status in Indonesia using Machine Learning Techniques
DOI:
https://doi.org/10.61179/infact.v10i01.753Keywords:
Poverty Classification, K-Nearest Neighbor, Socio-Economic Indicators, Machine Learning IndonesiaAbstract
This study explores the use of the K-Nearest Neighbor (KNN) algorithm to classify poverty status in Indonesia using publicly available socio-economic indicators. Traditional poverty classification methods are often inefficient and lack nuance. By leveraging the Knowledge Discovery in Databases (KDD) process, including data preprocessing, normalization, and dimensionality reduction via PCA, the study builds a robust classification model. The dataset includes indicators such as education, health, and expenditure levels from 514 districts/cities. The optimal KNN model, determined through cross-validation, achieved a test accuracy of 95.15%, with strong precision, recall, and ROC AUC scores. Feature importance analysis via Random Forest on PCA-transformed data highlights the predictive influence of certain component combinations. The results demonstrate the potential of machine learning to support more accurate and data-driven policy targeting in poverty alleviation. Future enhancements may involve integrating time-series or satellite data to increase relevance and precision.
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