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All Journal Infact: Jurnal Sains dan Komputer
Yudi Ahmad Hambali
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Computer Science & IT Electrical & Electronics Engineering

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Data-Driven Classification of Poverty Status in Indonesia using Machine Learning Techniques Syaila Fathia Azzahra; Yudi Ahmad Hambali; Ismail Marzuki Randos
Infact: International Journal of Computers Vol. 10 No. 01 (2026): Journal of Science and Computers
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/infact.v10i01.753

Abstract

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.
Co-Authors Ismail Marzuki Randos Syaila Fathia Azzahra