The Comparison of K-Nearest Neighbor (K-NN) Algorithm and Fuzzy Tsukamoto Logic in the Determination of SMA Students Majors in Banten

Abstract

Students’ majors at High School (SMA) in Banten is certainly different from the public ones which has already had its standardization and integrated computerized system in students’ majors. Most of private schools of SMA have not had a system and standardization in the students’ majors. Factors of majors ‘determination have not been based on students’ skill, interest, and aptitude. This present research aims at assessing two approaches in benefiting data of SMA majors. The first approach is by usingK-Nearest Neighbor (K-NN) algorithm, and the second one is by using Fuzzy Tsukamoto method. From the data assessment in SMA students’ majors, both of approaches (i.e., K-Nearest Neighbor (K-NN) algorithm and Fuzzy Tsukamoto method) can be used for SMA students’ majors with a fairly-high percentage of accuracy, of which K-NN98,4% and Tsukamoto 99,2%. This points out that both methods can be used for SMA students’ majors in Banten. From those both methods, the comparison is conducted by using three factors, that is, final result, data processing, and the speed of process by looking at the condition in the field, then it is found that Fuzzy Tsukamoto method is better than K-NN method in SMA students’ majors in Banten.