ANALISIS KOMPARASI ALGORITMA K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE DENGAN PENDEKATAN MULTI DATASET

Abstract

Data mining is a process of identifying data that is valid and has the potential to be useful to the person who did it. One of the purposes of data mining is to study previously existing data that composes certain patterns and is used to make predictions. Machine learning works by utilizing data and algorithms to create models with patterns from the data set. There are many algorithms that can be used, such as C4.5, K-Means, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Naïve Bayes, and others. Since there are many algorithms in data mining, each has its own advantages and disadvantages. This research will focus on the comparison between the Support Vector Machine algorithm and the K-Nearest Neighbor algorithm in terms of accuracy, precision and processing time.