PERBANDINGAN DATA UNTUK MEMPREDIKSI KETEPATAN STUDI BERDASARKAN ATRIBUT KELUARGA MENGGUNAKAN MACHINE LEARNING

  • Wijiyanto Wijiyanto Universitas Duta Bangsa Surakarta
  • Afu Ichsan Pradana Universitas Duta Bangsa Surakarta
  • Sopingi Sopingi Universitas Duta Bangsa Surakarta
DOI: https://doi.org/10.31000/jika.v8i2.10752

Abstract

Keberhasilan mahasiswa dalam menyelesaikan pendidikan tepat waktu merupakan tujuan yang penting. Berbagai faktor dapat memengaruhi keberhasilan ini, termasuk faktor non-akademik seperti data keluarga. Data yang digunakan berasal dari FIKOM-UDB dengan 365 record dan 11 atribut, di antaranya satu atribut berperan sebagai label (class). Data tersebut diproses menggunakan algoritma machine learning menggunakan pemodelan naïve bayes dan neural network. Sebelumnya, data dibagi menjadi data latih dan data uji dengan perbandingan prosentase yang berbeda, yaitu 90:10, 80:20, 70:30, 60:40, dan 50:50, untuk mencari kinerja terbaik berdasarkan nilai akurasi. Evaluasi menggunakan confusion matrix menghasilkan performa terbaik untuk naïve bayes dengan perbandingan 80:20, mencapai nilai akurasi sebesar 92%, precision 0.93, recall 0.98, dan F1-score 0.96. Sementara untuk neural network, performa terbaik terdapat pada perbandingan 50:50 dengan nilai akurasi sebesar 91%, precision 0.93, recall 0.97, dan F1-score 0.95. Hasil menunjukkan bahwa performa terendah untuk naïve bayes terjadi pada perbandingan 90:10, sementara untuk neural network terjadi pada perbandingan 80:20. Dengan demikian, algoritma naïve bayes menunjukkan performa yang lebih baik dibandingkan neural network sehingga, Fakultas dapat menerapkan model naïve bayes dalam memprediksi mahasiswa dalam rangka untuk mengantisipasi dan mengatasi permasalahan yang timbul terkait kelulusan mahasiswa dengan tepat waktu.

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Published
2024-04-30
Issue
Vol. 8 No. 2 (2024): JIKA (Jurnal Informatika)
Section
Articles

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