Hybrid Principal Component Analysis and K-Nearest Neighbor to Detect the Catfish Disease
D S Maylawati (1),(2),(*), R Andrian (1), S Sunarto (1), M Wildanuddin (1), A Wahana (1)

(1) Department of Informatics, UIN Sunan Gunung Djati Bandung, Indonesia
(2) Faculty of Information and Technology, Universiti Teknikal Malaysia Melaka, Malaysia

Abstract

Cathfish cultivation in Indonesia is a very promising business opportunity with a big profits. Every year, market demand continues to increase. However, this is contrary with the lack of catfish farmers knowledge, so that catfish yields are not optimal. This is because, for certain types of catfish such as Sangkuriang catfish, it is easy to contract certain diseases. This study aims to create an automated system that capable of detecting catfish disease based on its symptoms with image recognition techniques. Early detection of catfish disease can help to find out the causes and prevention, so that the yield remains optimally. The method that used in this study is Principal Component Analysis (PCA) for feature extraction in images combined with K-Nearest Neigbor (KNN) with Euclidean Distance to classify catfish diseases among others: white spots, edema (abdominal swelling), jaundice, and bent spinal disease (scoliosis and lordosis). Based on the results of the experiment using 30 images data for training and 20 images data for testing, 18 image data is classified correctly. This result proves that PCA and KNN able to detect catfish disease well with percentage of accuracy around 90%.

Keywords: Catfish, Data Mining, Euclidean Distance, Image Recognition, KNN, PCA

Topic: Computer Science

Link: https://ifory.id/abstract-plain/36HvrjnAZgpE

Web Format | Corresponding Author (Dian Saadillah Maylawati)