Akurasi Klasifikasi Kualitas Wine Menggunakan Algoritma Random Forest dengan Min-Max Normalization

Abstract

In this research, we will discuss the use of the Random Forest algorithm in classifying wine quality using Min-Max normalization. The data obtained will be subjected to data preprocessing and data normalization using Min-Max Normalization which is then applied to the Random Forest algorithm. This algorithm was chosen because it can provide good accuracy for the classification process. Data normalization and preprocessing are needed to produce a classification model with better accuracy. Min-Max normalization is used because it can improve the performance of the Random Forest algorithm in increasing accuracy.