Perbandingan Akurasi Algoritma K-Nearest Neighbor (KNN) dan Naive Bayes Dalam Memprediksi Waktu Kelulusan Mahasiswa. (Comparison of the Accuracy of K-Nearest Neighbor (KNN) and Naive Bayes Algorithms in Predicting Student Graduation Time).

Andriansyah, Muhammad Fajar (2023) Perbandingan Akurasi Algoritma K-Nearest Neighbor (KNN) dan Naive Bayes Dalam Memprediksi Waktu Kelulusan Mahasiswa. (Comparison of the Accuracy of K-Nearest Neighbor (KNN) and Naive Bayes Algorithms in Predicting Student Graduation Time). Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

Dalam sistem pendidikan, mahasiswa merupakan aset penting bagi lembaga pendidikan tinggi, dan untuk itu perlu diperhatikan waktu kelulusan mahasiswa. Oleh sebab itu, penting mengetahui prediksi lama waktu penyelesaian studi mahasiswa. Penelitian ini mengusulkan pembuatan sistem prediksi tingkat kelulusan mahasiswa agar dapat menjadi langkah preventif agar mahasiswa dapat memperbaiki proses belajar. Penelitian ini melakukan teknik Machine Learning yang dilakukan dengan tujuan untuk membandingkan algoritma K-Nearest Neighbor (KNN) dan Naïve Bayes. Percobaan penelitian dilakukan untuk menentukan model terbaik seperti, jumlah pengambilan data, jumlah kelas klasifikasi, dan handling imbalance class. Berdasarkan keseluruhan uji coba, metode KNN lebih unggul dibandingkan metode Naïve Bayes. Penerapan teknik oversampling SMOTE menghasilkan peningkatan yang signifikan dengan selisih nilai evaluasi (presisi, recall, F1 Score, dan akurasi) antara 12% sampai 41% pada metode Naïve Bayes maupun KNN. Hasil model prediksi 4 kelas menggunakan metode KNN dengan SMOTE mendapatkan nilai presisi sebesar 79%, nilai recall sebesar 78%, nilai F1 Score sebesar 78%, dan akurasi sebesar 78%. Sedangkan hasil prediksi 8 kelas menggunakan metode KNN dengan SMOTE mendapatkan nilai presisi, recall, F1 Score, dan akurasi sama-sama sebesar 93%.

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