Classifying Family Economic Status Using the K-Nearest Neighbor Algorithm in Popalia Village

Istrikah Istrikah; Rabiah Adawiyah; Yuwanda Purnamasari Pasrun

Authors

Istrikah Istrikah Universitas Sembilan Belas November Author
Rabiah Adawiyah Author
Yuwanda Purnamasari Pasrun Author

Keywords:

Classification, Family economic status, K-Nearest Neighbor, Confusion matrix, Village information system.

Abstract

Access to accurate family economic data is essential for the equitable distribution of village social assistance. At the Popalia Village Office, Tanggetada Sub-district, Kolaka Regency, identification of eligible recipients previously relied on manual, page-by-page verification of Statistics Indonesia (BPS) census documents, a process that was slow and often produced recipients that did not match the intended criteria. This study develops a web-based classification system using the K-Nearest Neighbor (KNN) algorithm to categorize 160 household heads into “Mampu” (financially capable) and “Tidak Mampu” (financially incapable) classes based on twelve socio-economic criteria, including occupation, monthly income, education, number of dependents, and asset ownership. The system was built following the Waterfall development model using PHP and MySQL with a use-case-driven UML design. Model performance was evaluated using Euclidean-distance-based KNN with 10-fold cross validation and confusion matrix analysis. The system achieved an average classification accuracy of 99.38% (minimum 93.75%, maximum 100%), a precision of 98.21%, a recall of 100%, and an F1-score of 99.10%. Black-box testing further confirmed that all functional modules operated as intended. These findings indicate that KNN is an accurate and practical method for supporting village-level social assistance targeting.

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