Evaluasi Kinerja Machine Learning dalam Memprediksi Kemampuan Adaptasi Mahasiswa pada Lingkungan Pembelajaran Daring

Penulis

  • Shindy Arti Universitas Negeri Jakarta
  • Elan Suherlan Universitas YARSI

DOI:

https://doi.org/10.55382/jurnalpustakaai.v5i1.901

Kata Kunci:

Tingkat Adaptasi Mahasiswa,, Pembelajaran Online, , Evaluasi Model machine learning, , neural network

Abstrak

Perkembangan kecerdasan buatan mendorong transformasi pembelajaran daring yang adaptif, di mana kemampuan adaptasi mahasiswa menjadi faktor krusial dalam keberhasilan belajar. Penelitian ini bertujuan mengevaluasi kemampuan algoritme machine learning dalam memprediksi tingkat adaptasi mahasiswa terhadap pembelajaran online. Data yang digunakan berasal dari tingkat pendidikan tinggi sebanyak 675 data mahasiswa, kemudian diolah menggunakan Python dan Orange 3.8. Metode pengembangan yang digunakan dalam penelitian ini merujuk pada metode CRISP-DM. Terdapat lima tahapan yang dilakukan yaitu kejelasan alur bisnis, pemahaman data, persiapan data, pemodelan dan evaluasi kinerja model. Dari tujuh algoritma supervised learning digunakan, algoritma Neural Network memiliki performa terbaik secara keseluruhan maupun per tingkat adaptasi (tinggi, sedang, rendah), dengan akurasi tertinggi sebesar 0,943. Penelitian ini juga mengidentifikasi faktor utama adaptasi menggunakan algoritma Decision Tree, dengan durasi kelas sebagai pembeda dominan. Rekomendasi penelitian selanjutnya mencakup seleksi fitur, penyeimbangan data, dan pengembangan sistem interaktif berbasis pengguna.

Unduhan

Data unduhan belum tersedia.

Biografi Penulis

Elan Suherlan, Universitas YARSI

 

 

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Diterbitkan

2025-04-30

Cara Mengutip

Arti, S., & Suherlan, E. . (2025). Evaluasi Kinerja Machine Learning dalam Memprediksi Kemampuan Adaptasi Mahasiswa pada Lingkungan Pembelajaran Daring. Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence), 5(1), 50–57. https://doi.org/10.55382/jurnalpustakaai.v5i1.901