KOMBINASI TOMEK-LINK DAN SMOTE UNTUK MENGATASI KETIDAKSEIMBANGAN KELAS PADA CREDIT CARD FRAUD

KOMBINASI TOMEK-LINK DAN SMOTE UNTUK MENGATASI KETIDAKSEIMBANGAN KELAS PADA CREDIT CARD FRAUD

• By Risdiansyah, Deni;
• Tesis
• 207

Meningkatnya aktivitas perdagangan secara online atau e-commerce telah menjadi trend saat ini. Akibatnya kejahatan yang paling sering terjadi adalah penipuan kartu kredit (credit card fraud) atau carding. Kurang lebih terdapat 1.000 kasus penipuan dalam satu juta transaksi sehingga data tersebut dikumpulkan dalam bentuk dataset
credit card fraud risk. Pada beberapa kasus, kelas minoritas justru lebih penting untuk diidentifikasi daripada kelas mayoritas seperti pada kasus transaksi credit card. Pada penelitian ini untuk menangani masalah ketidakseimbangan kelas pada dataset credit card fraud risk maka diusulkan metode resampling yaitu pendekatan
level data Tomek-Link dan SMOTE dengan model klasifikasi C5.0. Penelitian ini dilakukan untuk meningkatkan nilai akurasi AUC pada model algoritma klasifikasi C5.0. Hasil penelitian menunjukkan bahwa metode usulan mampu meningkatkan nilai AUC sebesar 0,134 dibandingkan tanpa metode resampling.

• Katakunci Ketidakseimbangan kelas , Oversampling, Tomek-Link, SMOTE, C5.0
• URL http://
• Penerbit ,

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