PERBANDINGAN KINERJA METODE KLASIFIKASI K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINES PADA DATASET PARKINSON

Abstract

The government program in the form of social assistance (bansos) is part of the effort to improve the welfare of the community and ensure basic needs and improve the standard of living of the recipients. However, there are often cases of mistargeting of social assistance programs by the government. Improper data management and Data Terpadu Kesejahteraan Sosial (DTKS) which are not used as the cause of the distribution of social assistance are not well targeted. The data can be analyzed using the classification method to determine whether or not the family accepts the ban from the government. This study classifies the SUSENAS data by comparing K-Nearest Neighbor (KNN) and Support Vector Machines (SVM). The advantage of the KNN method lies in the level of accuracy to solve problems with large data while the SVM method has better performance in various fields of application such as bioinformacs, handwriting recognition, text classification and so on. Based on training data and testing data comparison 85%:15% showed that KNN method had a better classification performance than the SVM method. The accuracy value of KNN method is 80,95% higher than the accuracy value of SVM method is 78,79%.