PENGEMBANGAN SISTEM REKOMENDASI ATLET ESPORTS BERDASARKAN PREDIKSI ELO RATING MENGGUNAKAN MODEL STOCHASTIC GRADIENT BOOSTING
DOI:
https://doi.org/10.22441/format.2022.v11.i2.006Keywords:
Bonus Demografi, Esports, ELO Rating, Regresi, Stochastic Gradient Boosting, Root Mean Squared ErrorAbstract
Indonesia memiliki tantangan bonus demografi dengan jumlah yang cukup besar. Tantangan tersebut dapat dimanfaatkan sebagai kesempatan yang baik khususnya untuk mendulang potensi Esports Indonesia. Sebanyak hampir seperempat total penduduk Indonesia dikategorikan sebagai penggiat video game dari populasi urban online. Namun potensi tersebut terhambat oleh kendala para pegiat esports yang kesulitan untuk menbentuk sebuah tim. Dalam penelitian ini mengembangkan sistem rekomendasi atlet esports untuk membantu para atlet dalam membentuk tim yang cocok. Dalam membuat sistem rekomendasi atlet dibutuhkan kecocokan antar atlet maka hal ini dapat diselesaikan menggunakan prediksi penilaian ELO Rating. Permasalahan prediksi ELO Rating dapat diselesaikan dengan model regresi Stochastic Gradient Boosting (SGB). Untuk menguji nilai prediksi, menggunakan Root Mean Squared Error (RMSE) dan performa berdasarkan waktu dengan satuan detik sebagai acuan. Dengan skenario pengujian data training berbanding dengan data testing dengan rasio 90%:10%, menghasilkan RMSE Testing sebesar 17.7422 dan RMSE Training sebesar 16.9820 sehingga model ini tidak mengalami overfitting dikarenakan memiliki selisih yang kecil yaitu sebesar 0.7602 serta memakan waktu pelatihan sebesar 8.6720 detik.
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