Analitik Prediktif Sepakbola: Model Machine Learning BRI Liga 1 Indonesia

Rodhi Rusdianto Hidayat; Ermelinda Yersin Putri Larung; Evi Sinaga; Ansar CS; Ibrahim Ibrahim; Ipa Sari Kardi; Miftah Fariz Prima Putra; Asmathil Bari Samal; Hariyanto Babingga

doi:10.20527/multilateral.v23i4.21889

Analitik Prediktif Sepakbola: Model Machine Learning BRI Liga 1 Indonesia

Rodhi Rusdianto Hidayat, Ermelinda Yersin Putri Larung, Evi Sinaga, Ansar CS, Ibrahim Ibrahim, Ipa Sari Kardi, Miftah Fariz Prima Putra, Asmathil Bari Samal, Hariyanto Babingga

Abstract

Penelitian ini bertujuan untuk mengembangkan dan memvalidasi model prediktif berbasis machine learning guna memprediksi hasil pertandingan BRI Liga 1 Indonesia musim 2023-2024. Dengan menggunakan algoritma XGBoost untuk klasifikasi hasil pertandingan dan Gradient Boosting Regressor untuk prediksi skor, penelitian ini menganalisis berbagai variabel seperti penguasaan bola, akurasi tembakan, jumlah umpan sukses, tendangan sudut, serta faktor lain yang berkontribusi terhadap hasil pertandingan. Data pertandingan dikumpulkan dari sumber resmi dan diproses menggunakan metode statistik serta teknik Machine Learning. Hasil penelitian menunjukkan bahwa model XGBoost mencapai akurasi sebesar 98.4% dengan precision, recall, dan F1-score yang hampir sempurna. Model Gradient Boosting Regressor juga menunjukkan performa yang sangat baik dengan R-squared score sebesar 0.992 dan tingkat kesalahan yang sangat rendah. Kesimpulan dari penelitian ini menegaskan bahwa model prediktif berbasis analitik dapat menjadi alat yang andal dalam mendukung strategi tim sepak bola, meningkatkan performa permainan, dan membantu pengambilan keputusan berbasis data dalam kompetisi profesional. Penelitian ini merekomendasikan pengembangan lebih lanjut dengan memasukkan variabel eksternal seperti kondisi cuaca, cedera pemain, serta penggunaan model deep learning untuk meningkatkan akurasi prediksi.

Keywords

analitik prediktif; machine learning; sepak bola; BRI Liga 1; model prediksi

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DOI: http://dx.doi.org/10.20527/multilateral.v23i4.21889