PERBANDINGAN METODE K-NEAREST NEIGHBOUR DAN FUZZY K-NEAREST NEIGHBOUR DALAM KLASIFIKASI DATA

SAPUTRA, RIO ANDIKA and Primartha, Rifkie and Saputra, Danny Matthew (2019) PERBANDINGAN METODE K-NEAREST NEIGHBOUR DAN FUZZY K-NEAREST NEIGHBOUR DALAM KLASIFIKASI DATA. Undergraduate thesis, Sriwijaya University.

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Abstract

K-NN is such an effective and the most commonly used method for data classification. This method also believed as one of the most popular and easiest method to be implemented in this subject. But, there is also a method that has a lot of similarities with this one ( K-NN ) and known for its ability to determine an object category also by its weight, this method called Fuzzy K-NN. Fuzzy K-Nearest Neighbour basically is just the K-NN with Fuzzy theory in it. The only difference between this two is the ability of Fuzzy K-NN to determines the membership value in each individuals in data classification process. in this study, writer tried to examine the affects of weights ( leads to membership value ) on the accuracy of the data classification ( in each K ) by using iris and blood transfusion data as the objects. Based on the research results, it was found that the best accuracy for both K-NN and Fuzzy K-NN are 98,04% for iris data, with K = 1 in K-NN and K = 13 in Fuzzy K-NN. Meanwhile, in blood transfusion data classification the best results for both methods shows that K-NN has a better accuracy ( 80,54% ) than the Fuzzy K-NN ( 78,95% ) in K = 20. Keywords : Classification, K-nearest neighbour, Fuzzy K-nearest neighbour, weights, Membership value

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Klasifikasi Data,K-nearest neighbour, fuzzy K-nearest neighbour
Subjects:	Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76.9.D343 Data mining. Database searching. Big data. Q Science > QA Mathematics > QA1-939 Mathematics > QA9.64.A56 Computer science. Fuzzy mathematics.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	Users 10481 not found.
Date Deposited:	01 Feb 2021 08:01
Last Modified:	01 Feb 2021 08:01
URI:	http://repository.unsri.ac.id/id/eprint/41368

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