Klasifikasi Sepatu Sepak Bola Berdasarkan Peminatan, Brand, Dan Posisi Pemain Dengan Machine Learning

Robby Kurniawan Syahfitra; Farid Akbar Siregar

Authors

Robby Kurniawan Syahfitra Universitas Muhammadiyah Sumatera Utara
Farid Akbar Siregar Universitas Muhammadiyah Sumatera Utara

Keywords:

Klasifikasi, Sepatu Sepak Bola, Machine Learning, Random Forest, SMOTE

Abstract

Pemilihan sepatu sepak bola yang tepat memengaruhi kenyamanan dan performa pemain, namun pemain amatir hingga semi-profesional sering mengalami kesulitan menentukan tipe sepatu yang sesuai karena kuatnya pengaruh merek dan kurangnya pertimbangan aspek teknis. Penelitian ini bertujuan mengklasifikasikan tipe sepatu sepak bola berdasarkan peminatan, brand, dan posisi pemain menggunakan algoritma Random Forest. Dataset awal berjumlah 3.971 data sepatu sepak bola. Setelah pembersihan data dan penanganan nilai kosong diperoleh 3.803 data, kemudian dibagi menjadi 3.042 data latih dan 761 data uji. Ketidakseimbangan kelas pada data latih ditangani menggunakan Synthetic Minority Over-sampling Technique (SMOTE) sehingga jumlah data latih menjadi 12.712. Hasil pengujian menunjukkan akurasi 99,61%, dengan 758 dari 761 data uji diklasifikasikan secara benar. Hasil feature importance menunjukkan BootsName sebagai fitur paling berpengaruh, diikuti Brand_BootsName dan BootsPosition. Model menunjukkan performa klasifikasi sangat baik. Model kemudian diimplementasikan dalam prototipe sistem berbasis web SMART BOOTS untuk menghasilkan klasifikasi dan rekomendasi sepatu berdasarkan karakteristik yang dimasukkan pengguna

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