Klasifikasi Status Drop Out Mahasiswa Menggunakan Naïve Bayes dengan Seleksi Fitur Information Gain

Authors

  • Nurissaidah Ulinnuha UIN Sunan Ampel Surabaya
  • Aris Fanani UIN Sunan Ampel Surabaya

DOI:

https://doi.org/10.33633/tc.v22i4.9004

Keywords:

drop out, Information Gain, Naive Bayes, klasifikasi

Abstract

Salah satu masalah dalam perguruan tinggi adalah kasus drop out. Banyaknya mahasiswa yang mengalami drop out merupakan salah satu indikator yang mempengaruhi kualitas pembelajaran dan akreditasi yang sangat penting untuk keberlangsungan suatu institusi. Penelitian ini menerapkan algoritma Naive Bayes dan algoritma seleksi fitur Information Gain untuk melakukan klasifikasi mahasiswa drop out.  Hasil penelitian menunjukkan bahwa dua fitur yang berpengaruh paling signifikan terhadap klasifikasi mahasiswa drop out adalah jumlah SKS yang ditempuh dan nilai IPS pada semester 4. Temuan ini menunjukkan bahwa aspek akademik yang terkait dengan perkembangan studi mahasiswa berpengaruh dalam klasifikasi drop out. Penerapan seleksi fitur menggunakan metode Information Gain berhasil meningkatkan akurasi dan presisi dari model Naïve Bayes. Model yang dihasilkan mencapai akurasi sebesar 98.36%, presisi sebesar 88.37%, dan recall sebesar 97.44%. Hasil ini menunjukkan bahwa seleksi fitur dengan metode Information Gain membantu dalam mengidentifikasi fitur-fitur penting yang berkontribusi terhadap kualitas model klasifikasi.

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Published

2023-11-28

Issue

Vol. 22 No. 4 (2023): November 2023

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