Klasifikasi Tingkat Kemiskinan di Indonesia Menggunakan Metode Naïve Bayes

F Fathoni(1*), Zahwa Aulia Prayetno(2), Michael Joenathan Darwin(3), Liza Athalya Nurjannah(4),

(1) Universitas Sriwijaya, Palembang, Indonesia
(2) Universitas Sriwijaya, Palembang, Indonesia
(3) Universitas Sriwijaya, Palembang, Indonesia
(4) Universitas Sriwijaya, Palembang, Indonesia
(*) Corresponding Author

Abstract


The purpose of this study is to design a poverty level classification model in Indonesia using this Naïve Bayes algorithm, with a specific focus on identifying key socio-economic variables that significantly influence poverty status. The development of this model is aimed at laying the groundwork for supporting policy decisions in the distribution of social assistance, ensuring that aid is more accurately targeted toward communities truly in need. The dataset employed in this research includes various relevant socio-economic indicators, and the model's performance is validated through a cross-validation method to assess its accuracy and generalization capability. The outcomes reveal that the model obtained an accuracy of 92.40% ± 2.99%, with high precision for the majority class. However, the model still shows limitations in classifying the minority class or vulnerable groups. Overall, the findings suggest that the Naïve Bayes algorithm holds strong potential for poverty classification and can be utilized as a decision-support tool in the formulation of data-driven social policies, particularly in optimizing the fair and effective distribution of social aid.

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DOI: https://doi.org/10.30645/kesatria.v6i2.589

DOI (PDF): https://doi.org/10.30645/kesatria.v6i2.589.g584

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