KNN vs Naive Bayes Untuk Deteksi Dini Putus Kuliah Pada Profil Akademik Mahasiswa
Abstract
Penelitian ini membahas bagaimana perbandingan KNN dan Naive Bayes dalam memprediksi potensi putus kuliah pada mahasiswa. Data yang dijadikan variabel independen adalah data akademik yaitu nilai semester 1 hingga 6. Hasil dari penelitian ini diharapkan menjadi pedoman dalam menerapkan algoritma ke dalam sistem deteksi dini putus kuliah. Algoritma-algoritma ini diterapkan dengan library Scikit-learn pada Python. Nilai akurasi yang dihasilkan dari penelitian ini menunjukkan Naive Bayes (92%) lebih unggul dalam memprediksi status putus kuliah mahasiswa dibandingkan dengan algoritma KNN (85%). Namun perlu dilakukan penelitian lanjutan lagiuntuk menguji konsistensi dan akurasi pada data yang lebih besar dan lebih beragam.
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DOI: http://dx.doi.org/10.30872/jurti.v3i2.3097
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