Perbandingan Naive Bayes dan Support Vector Machine dalam Klasifikasi Tingkat Kemiskinan di Indonesia - Mukharyahya, Zulfa Alviandri; Astuti, Yani Parti; Cahyani, Okta Nur (Universitas Hamzanwadi, 2025)
Poverty in Indonesia is a complex issue influenced by various economic and socio-cultural factors. This study aims to compare the performance of Naïve Bayes and Support Vector Machine (SVM) in classifying poverty levels in Indonesia while also evaluating the effectiveness of random oversampling in addressing data imbalance. The dataset consists of 514 samples from various districts and cities in Indonesia, with 452 samples classified as