Oversampling Sintetis Berbasis Kopula untuk Model Klasifikasi dengan Data yang Tidak Seimbang
DOI:
https://doi.org/10.26593/jrsi.v12i1.6380.1-10Kata Kunci:
oversampling sintetis, kopula, model klasifikasi, distribusi Metalog. K-Nearest NeighborAbstrak
Model klasifikasi berbasis pembelajaran mesin untuk mendeteksi anomali biasanya didasarkan pada data dengan proporsi yang tidak seimbang. Proporsi data anomali biasanya jauh lebih kecil dibandingkan proporsi data non anomali. Ketidakseimbangan data menyebabkan model klasifikasi lebih banyak melakukan pembelajaran dengan data non anomali sehingga model bisa bias. Salah satu metode yang banyak digunakan untuk mengatasi masalah ini adalah oversampling sintetis. Oversampling sintetis umumnya didasarkan pada jarak dan didominasi metode berbasis k-Nearest Neighbor. Secara umum, pola data bisa berdasarkan jarak atau hubungan korelasional. Penelitian ini bertujuan menawarkan metode oversampling sintetis berdasarkan hubungan korelasional dalam bentuk distribusi probabilitas bersama dari data aslinya. Distribusi probabilitas bersama direpresentasikan dengan kopula Gaussian, sedangkan distribusi probabilitas marjinalnya direpresentasikan menggunakan tiga alternatf distribusi, yaitu sistem distribusi Pearson, distribusi empiris, dan sistem distribusi Metalog. Metode ini dibandingkan dengan beberapa metode oversampling lain yang umum digunakan untuk data yang tidak seimbang. Implementasi dilakukan dalam masalah kredit macet nasabah kartu kredit di suatu bank dengan metode klasifikasi k-Nearest Neighbor dengan ukuran kinerja akurasi total dengan metode validasi k-fold cross validation. Didapati bahwa model klasifikasi dengan metode oversampling usulan menggunakan distribusi marjinal Metalog memiliki akurasi total tertinggi.
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