MODEL OPTIMISASI HYBRID ENSEMBLES DALAM MENYELESAIKAN PERMASALAHAN CLASS IMBALANCE
DOI:
https://doi.org/10.29103/techsi.v8i2.134Abstract
Objek penelitian yang akan diteliti pada penelitian ini adalah permasalahan class imbalance yang merupakan salah satu permasalahan utama di dalam klasifikasi data. Salah satu metode yang dapat dipakai untuk menyelesaikan permasalahan class imbalance adalah Hybrid Ensembles. Namun, terdapat 2 (dua) isu utama yang perlu dibahas yaitu masalah jumlah classifier dan diversity data. Peneliti di dalam penelitian akan melakukan proses optimisasi terhadap hybrid ensembles. Peneliti akan mengemukakan model yang menambahkan ensemble learning berbasis sample (sample based). Di dalam model ini peneliti akan melakukan penggabungan penggunaan bagging dengan metode DCS (Differenct Contribution Sampling) yang terbukti dapat mengurangi jumlah classifier dan meningkatkan diversity data dibandingkan dengan menggunakan AdaBoost. Model ini juga akan mengunakan Random Balance Ensemble Method yang menggabungkan random undersampling dengan SMOTEBoost yang dapat digunakan tahapan prepocessing yang ditujukan untuk mengurangi ukuran data training dan meningkatkan diversity dari dataset.
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