Deteksi Kecurangan Transaksi Menggunakan Random Forest Classification untuk Analisis Transaksi Online
Keywords:
deteksi kecurangan, Random Forest, transaksi online, klasifikasi, ketidakseimbangan dataAbstract
Pertumbuhan transaksi digital meningkatkan peluang terjadinya kecurangan transaksi, sehingga diperlukan model deteksi yang mampu bekerja cepat, akurat, dan dapat dijelaskan. Penelitian ini bertujuan menerapkan algoritma Random Forest Classification untuk mendeteksi kecurangan transaksi online berdasarkan data transaksi sekunder. Data yang digunakan adalah Credit Card Fraud Dataset yang memuat fitur jarak transaksi, rasio harga transaksi terhadap median pembelian, penggunaan chip, penggunaan PIN, transaksi online, dan label fraud. Tahapan penelitian mencakup pemahaman masalah, data overview, exploratory data analysis, pembersihan data, penanganan outlier, pembagian data 80:20, pelatihan Random Forest Classifier, evaluasi model, serta analisis feature importance. Rumus yang digunakan meliputi voting mayoritas Random Forest, Gini Index, penurunan impurity, accuracy, precision, recall, F1-score, TPR, FPR, AUC, dan feature importance. Hasil penelitian menunjukkan akurasi sebesar 98% dengan AUC 0,87. Model sangat baik mengenali transaksi tidak curang dengan precision 97% dan recall 100%, tetapi recall pada kelas curang masih 72% karena terdapat 4.870 false negative. Fitur ratio_to_median_purchase_price menjadi variabel paling dominan, diikuti online_order dan repeat_retailer. Hasil ini menunjukkan bahwa Random Forest berpotensi menjadi alat bantu deteksi kecurangan transaksi online, meskipun peningkatan recall kelas fraud tetap perlu dilakukan.
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