Open AccessAnalisis Matthew Correlation Coefficient pada K-Nearest Neighbor dalam Klasifikasi Ikan Hias
Author(s) -
Novia Hasdyna,
Rozzi Kesuma Dinata
Publication year - 2020
Publication title -
informal
Language(s) - English
Resource type - Journals
ISSN - 2503-250X
DOI - 10.19184/isj.v5i2.18907
Subject(s) - correlation coefficient , mathematics , euclidean distance , pearson product moment correlation coefficient , k nearest neighbors algorithm , statistics , correlation , value (mathematics) , combinatorics , artificial intelligence , computer science , geometry
K-Nearest Neighbor (K-NN) is a machine learning algorithm that functions to classify data. This study aims to measure the performance of K-NN algorithm by using Matthew Correlation Coefficient (MCC). The data that used in this study are the ornamental fish which consisting of 3 classes named Premium, Medium, and Low. The analysis results of the Matthew Correlation Coefficient on K-NN using Euclidean Distance obtained the highest MCC value in Medium class which is 0.786542. The second highest MCC value is in Premium class which is 0.567434. The lowest MCC value is in Low class which is 0.435269.
Overall, the MCC values is statistically which is 0,596415.