Klasifikasi Tingkat Kemampuan Adaptasi Siswa dalam Pembelajaran Online Menggunakan Decision Tree

Authors

  • Asmaul Lailiyah Universitas Muhammadiyah Malang
  • Vinna Rahmayanti Setyaning Nastiti Universitas Muhammadiyah Malang
  • Evi Dwi Wahyuni Universitas Muhammadiyah Malang
  • Christian Sri Kusuma Aditya Universitas Muhammadiyah Malang

DOI:

https://doi.org/10.62411/tc.v23i1.9739

Keywords:

Pembelajaran Mesin, Decision Tree, Feature Engineering, Kemampuan Beradaptasi Siswa

Abstract

Kemajuan dalam ilmu pengetahuan dan teknologi mendorong adaptasi terhadap pemanfaatan teknologi di berbagai sektor, seperti komunikasi, pendidikan, dan informasi. Terutama dalam konteks teknologi pendidikan, dapat diamati bahwa pembelajaran online sedang mendapatkan popularitas yang signifikan di berbagai lembaga pendidikan. Oleh karena itu, penting untuk mengeksplorasi seberapa baik peserta didik dapat beradaptasi dengan lingkungan pembelajaran online. Memprediksi tingkat adaptasi peserta didik memiliki signifikansi yang besar bagi pendidik dan pengembang platform pembelajaran online, dengan tujuan meningkatkan efisiensi dan kualitas pengalaman belajar. Penelitian ini menggunakan dataset “Students Adaptability Level in Online Education” dengan menerapkan pendekatan Algoritma Decision Tree. Hasil penelitian memperoleh akurasi sebesar 95%, meningkat 7,44% dari penelitian sebelumnya yang hanya memperoleh akurasi sebesar 87,56% dengan menggunakan algoritma yang sama tanpa Feature Engineering. Hal ini menunjukkan bahwa Feature Engineering memegang peranan penting dalam klasifikasi tingkat kemampuan adapatasi siswa untuk mendapatkan hasil yang baik dengan akurasi yang tinggi.

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Published

2024-02-18