FINAL GRADE PREDICTION MODEL BASED ON STUDENT'S ALCOHOL CONSUMPTION

  • rangga ramadhan saelan (1*) Universitas Nusa Mandiri
  • Siti Masturoh (2)
  • Taopik Hidayat (3)
  • Siti Nurlela (4)
  • Risca Lusiana Pratiwi (5)
  • Muhammad Iqbal (6)

    • (*) Corresponding Author
DOI: https://doi.org/10.33480/techno.v19i1.3056

Keywords: Model, MAE, Parameter, Machine, Learning, Nilai

Abstract

Untuk mengetahui pengaruh konsumsi alcohol dan dan beberapa faktor lainnya yang diperkirakan memiliki peran terhadap tingkat kinerja belajar remaja yang masih bersekolah, maka saat ini dilakukan penelitian terhadap data publik yang telah didapatkan dengan menggunakan teknik machine learning dengan melatih beberapa model untuk memprediksi nilai akhir sebagai acuan kinerja belajar pelajar. Dengan melatih beberapa model machine learning untuk memprediksi nilai tahun akhir dari bahasa portugal dengan melakukan metode komparatif membandingkan model Support Vector Regressor (SVR) dan Random Forest (RF) sehingga akan didapatkan model terbaik untuk memprediksi. Semua model memiliki hyperparameter yang harus disesuaikan. Untuk menyetel hyperparameter ini menggunakan menggunakan Cross Validation. Model terbaik untuk memprediksi nilai akhir G3 adalah Support Vector Regressor (SVR) dan Random Forest (RF), dan memiliki mean absolute error (MAE) masing-masing sekitar 2,24 dan 2,25. Melalui plot MAE, model SVR dan RF bekerja dengan baik. Tetapi, Dengan menganalisis distribusi kesalahan yang dibuat oleh kedua model, dapat disimpulkan bahwa SVR lebih seimbang, yaitu memiliki rasio yang lebih baik antara nilai yang diremehkan dan ditaksir terlalu tinggi, sementara RF berkinerja lebih baik pada outlier.

Published
2022-07-18
How to Cite
saelan, rangga, Masturoh, S., Hidayat, T., Nurlela, S., Pratiwi, R., & Iqbal, M. (2022). FINAL GRADE PREDICTION MODEL BASED ON STUDENT’S ALCOHOL CONSUMPTION. Jurnal Techno Nusa Mandiri, 19(1), 61 - 68. https://doi.org/10.33480/techno.v19i1.3056
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Vol 19 No 1 (2022): Techno Nusa Mandiri : Journal of Computing and Information Technology Period of March 2022
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