Identification of Bacilli Bacteria in Acute Respiratory Infection (ARI) using Learning Vector Quantization

Fitri, Zilvanhisna Emka and Sahenda, Lalitya Nindita and Puspitasari, Pramuditha Shinta Dewi and Imron, Arizal Mujibtamala Nanda (2022) Identification of Bacilli Bacteria in Acute Respiratory Infection (ARI) using Learning Vector Quantization. Advances in Social Science, Education and Humanities Research, 645. pp. 26-32. ISSN 2352-5398

	Text (cek turnitin) 12.Atlantis Press_Identification of Bacilli Bacteria in Acute Respiratory Infection (ARI) using Learning Vector Quantization.pdf - Supplemental Material Available under License Creative Commons Attribution Share Alike. Download (1MB)
	Text (full paper) 12.Atlantis Press,2022.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Two diseases that include Acute Respiratory Infections (ARI) are diphtheria and tuberculosis. Both diseases have a large number of sufferers and can cause extraordinary events (KLB). One of the achievement indicators of infectious disease control and management programs is discovery. However, the limited number of medical analysts causes the discovery process (examination) long and subjective. To help with this problem, a bacillus identification system was created for early detection of Acute Respiratory Infections (ARI). This system is an implementation of computer vision. The data used are preparations of the bacteria Mycobacterium tuberculosis and Corynebacterium diphtheriae obtained at Besar Laboratorium Kesehatan (BBLK) Surabaya. The parameters used are the area, perimeter and shape factor. The Learning Vector Quantization (LVQ) method can classify and identify bacillus bacteria that cause acute respiratory infections with a training accuracy of 97% and a test accuracy of 86% with a learning rate of 0.01 and a reduced learning rate of 0.25.

Actions (login required)

View Item