Implementasi Data Mining Untuk Memprediksi Calon Legislatif Terpilih Daerah Pemilihan Provinsi Banten Dengan Algoritma Naive Bayes Classification
Keywords:
Data Mining, Naive Bayes, Pemilihan Umum, Anggota Legislatif, Prediksi, AkurasiAbstract
Indonesia adheres to a Presidential system of government, where the selection of potential leaders and representatives typically involves the holding of General Elections (Pemilu). The purpose of this research is to utilize data mining and the Naive Bayes method to predict the elected legislative candidates in the provincial electoral districts of Banten in the year 2019. The data for this study was obtained from the KPU website, specifically focusing on the data of legislative candidates (DPR RI and DPRD Provincial) in the provincial electoral districts of Banten for the year 2019. The research results demonstrate a remarkable accuracy rate of 95%, providing valuable strategic insights for legislative candidates and political parties during election campaigns. Through data mining research employing the Naive Bayes method, it is anticipated that campaign effectiveness can be enhanced, offering valuable insights for future elections.
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