Sebuah Tinjauan Literatur Sistem Implementasi Machine Learning untuk Prediksi Hasil Pertandingan Sepak Bola

Fadhilla Nurrahman; Regiolina Hayami

doi:10.55382/jurnalpustakadata.v6i4.2077

Penulis

Fadhilla Nurrahman Universitas Muhammadiyah Riau
Regiolina Hayami Universitas Muhammadiyah Riau

DOI:

https://doi.org/10.55382/jurnalpustakadata.v6i4.2077

Kata Kunci:

prediksi sepak bola, machine learning, deep learning, ystematic literature review, analitik olahraga

Abstrak

Prediksi hasil pertandingan sepak bola merupakan permasalahan yang kompleks karena dipengaruhi oleh banyak faktor yang saling berinteraksi serta tingkat ketidakpastian yang tinggi. Seiring dengan berkembangnya analitik data dan kecerdasan buatan, berbagai pendekatan statistik, machine learning, dan deep learning telah diterapkan untuk meningkatkan kinerja prediksi hasil pertandingan sepak bola. Penelitian ini bertujuan untuk melakukan Systematic Literature Review (SLR) terhadap studi-studi yang membahas prediksi hasil pertandingan sepak bola guna mengidentifikasi pendekatan dan algoritma yang paling banyak digunakan, menganalisis jenis fitur dan sumber data yang dimanfaatkan, serta membandingkan kinerja dan keterbatasan metode yang diusulkan. Metode penelitian dilakukan dengan mengumpulkan dan menyeleksi jurnal ilmiah nasional dan internasional yang relevan, kemudian menganalisisnya secara sistematis berdasarkan pendekatan, fitur, dan hasil yang dilaporkan. Hasil tinjauan menunjukkan bahwa machine learning klasik dan metode ensemble merupakan pendekatan yang paling dominan dan konsisten digunakan. Selain itu, kualitas dan relevansi fitur, khususnya pemanfaatan data berbasis peristiwa dan data performa pemain, memiliki pengaruh signifikan terhadap kinerja model prediksi. Namun, tidak terdapat satu metode yang secara universal unggul dalam seluruh konteks penelitian. Oleh karena itu, diperlukan keseimbangan antara kinerja prediksi, interpretabilitas model, dan ketersediaan data dalam pengembangan penelitian selanjutnya.

Unduhan

Data unduhan belum tersedia.

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