Prediksi Kemampuan Squat Atlet Powerlifting Pria Menggunakan XGBoost dan LightGBM
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Abstract
Machine learning semakin penting di era modern karena kemampuannya menghasilkan prediksi hanya dengan mengolah data. Penelitian ini bertujuan untuk memprediksi kemampuan squat atlet powerlifting pria menggunakan dua algoritma pembelajaran mesin, yaitu XGBoost dan LightGBM. Kedua algoritma dipilih karena keduanya mampu menangani data berukuran besar dengan variabel prediktor yang kompleks. Data yang digunakan mencakup informasi fisik atlet, catatan performa squat, bench press, deadlift, serta variabel terkait kompetisi. Metode penelitian meliputi tahap pra-pemrosesan data, pembagian data ke dalam set pelatihan dan pengujian, penerapan algoritma XGBoost dan LightGBM, serta evaluasi performa model. Evaluasi dilakukan menggunakan metrik root mean square error (RMSE), koefisien determinasi (R²), dan mean absolute error (MAE). Hasil penelitian menunjukkan bahwa kedua algoritma mampu memberikan performa prediksi yang baik, dengan XGBoost lebih unggul dalam tingkat kinerja prediksi yang lebih tinggi dibandingkan dengan LightGBM.
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