Analisis Matthew Correlation Coefficient pada K-Nearest Neighbor dalam Klasifikasi Ikan Hias

  • Novia Hasdyna
  • Rozzi Kesuma Dinata Lecture

Abstract




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.




Published
2020-08-30
How to Cite
HASDYNA, Novia; DINATA, Rozzi Kesuma. Analisis Matthew Correlation Coefficient pada K-Nearest Neighbor dalam Klasifikasi Ikan Hias. INFORMAL: Informatics Journal, [S.l.], v. 5, n. 2, p. 57 - 64, aug. 2020. ISSN 2503-250X. Available at: <https://jurnal.unej.ac.id/index.php/INFORMAL/article/view/18907>. Date accessed: 18 apr. 2024. doi: https://doi.org/10.19184/isj.v5i2.18907.