Open AccessIdentification and Classification of Pathogenic Bacteria Using the K-Nearest Neighbor Method
Author(s) -
Dwi Rahmawati,
Mutiara Puspa Putri,
Miftahul Ulum,
Koko Joni
Publication year - 2021
Publication title -
jeee-u (journal of electrical and electronic engineering-umsida)/jte-u (jurnal teknik elektro universitas muhammadiyah sidoarjo)
Language(s) - English
Resource type - Journals
eISSN - 2540-8658
pISSN - 2460-9250
DOI - 10.21070/jeeeu.v5i1.1221
Subject(s) - pathogenic bacteria , bacteria , identification (biology) , k nearest neighbors algorithm , artificial intelligence , pattern recognition (psychology) , microbiology and biotechnology , similarity (geometry) , biology , computer science , image (mathematics) , genetics , botany
Bacteria are a group of living things or organisms that do not have a core covering. In the grouping, some bacteria are pathogenic. With a microscopic size, many pathogenic bacteria are found around and spread through the food eaten or by touching objects around them, then cause diseases such as diarrhea, vomiting, and others. As a more effective effort to help the government and society prevent disease caused by pathogenic bacteria, a system for the identification and classification of pathogenic bacteria K-Nearest Neighbor was created. This system uses a biological microscope that is attached to a webcam camera above the ocular lens as a tool to see bacterial objects and assist in bacterial capture. Rough player rotates automatically (auto-focus) in image capture. In the process of classification and identifying bacteria, the K-Nearest Neighbor method is used, which is a method with the calculation of the nearest neighbor or calculation based on the level of similarity to the dataset. In this study, the bacteria vibrio chlorae, staphylococcus aereus, and streptococcus m. with the highest accuracy is the K = 9 value of 97.77% using the Chebyshev method.