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Penerapan Data Mining Menggunakan Metode Naïve Bayes Untuk Menentukan Faktor Yang Mempengaruhi Kelulusan Dan Ketidaklulusan Mahasiswa Di Universitas Pelita Bangsa Edy Widodo; Ditya Lambang Setyawan
Prosiding Sains dan Teknologi Vol. 3 No. 1 (2024): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 3 - Januari 2024
Publisher : DPPM Universitas Pelita Bangsa

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Abstract

Data mining is the process of discovering patterns from datasets to generate information that can be used for prediction based on historical data. This study aims to analyze the factors influencing student graduation and non-graduation at Universitas Pelita Bangsa using the Naïve Bayes method. Data processing was conducted through manual calculations, Microsoft Excel, and RapidMiner, producing consistent evaluation results with an accuracy of 68.18%, precision of 33.33%, and recall of 16.67%. The findings indicate that the Naïve Bayes method can be effectively applied to predict student graduation factors with acceptable accuracy, making it a suitable approach for analyzing graduation data and supporting academic decision-making processes.
Co-Authors Edy Widodo