ANALISIS KETIDAKSEIMBANGAN KELAS DALAM PENGEMBANGAN MODEL KLASIFIKASI
Abstract
Ketidakseimbangan kelas atau imbalance class merupakan masalah yang sering terjadi dalam proses pembelajaran mesin, ada banyak pendekatan atau metode yang digunakan untuk menyelesaikan permasalahan tersebut, pada penelitian ini terdapat sebuah dataset memiliki kelas yang tidak seimbang, dimana untuk mengatasi ketidakseimbangan kelas tersebut penelitian ini melakukan pendekatan dengan cara menggabungkan kelas-kelas minoritas. nantinya kelas-kelas minoritas tersebut akan di evaluasi dengan model pembelajaran mesin yang menggunakan delapan algoritma berbeda yaitu Algoritma AdaBoost, Algoritma Gradient Boosting, Algoritma kNN, Algoritma Naïve Bayes, Algoritma Neural Network, Algoritma Random Forest, Algoritma SVM, dan Algoritna Decicion Tree. selain di evaluasi dengan delapan algoritma data tersebut juga akan diterapkan pendekatan oversampling dan undersampling. dari experimental tersebut diharapkan kita dapat melihat hasil evaluasi dari model pembelajaran mesin. Eksperimen ini diharapkan dapat memberikan wawasan tentang efektivitas metode oversampling dan undersampling dalam meningkatkan kinerja model pada dataset dengan ketidakseimbangan kelas. Hasil dari eksperimen ini akan memberikan gambaran yang lebih jelas tentang bagaimana mengatasi ketidakseimbangan kelas dalam konteks tertentu, serta memberikan pemahaman yang lebih mendalam tentang performa model pembelajaran mesin pada dataset tersebut
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