Proceedings of the 2nd International Conference on Social Science, Humanity and Public Health (ICOSHIP 2021)

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

Authors
Zilvanhisna Emka Fitri1, *, Lalitya Nindita Sahenda2, Pramuditha Shinta Dewi Puspitasari3, Arizal Mujibtamala Nanda Imron4
1,2,3Department of Information Technology, Politeknik Negeri Jember, Indonesia
4Department of Electrical Engineering, Universitas Jember, Indonesia
*Corresponding author. Email: zilvanhisnaef@polije.ac.id
Corresponding Author
Zilvanhisna Emka Fitri
Available Online 17 February 2022.
DOI
10.2991/assehr.k.220207.005How to use a DOI?
Keywords
acute respiratory infections; bacilli bacteria; computer vision; learning vector quantization
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.

Copyright
© 2022 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the 2nd International Conference on Social Science, Humanity and Public Health (ICOSHIP 2021)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
17 February 2022
ISBN
10.2991/assehr.k.220207.005
ISSN
2352-5398
DOI
10.2991/assehr.k.220207.005How to use a DOI?
Copyright
© 2022 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

risenwbib
TY  - CONF
AU  - Zilvanhisna Emka Fitri
AU  - Lalitya Nindita Sahenda
AU  - Pramuditha Shinta Dewi Puspitasari
AU  - Arizal Mujibtamala Nanda Imron
PY  - 2022
DA  - 2022/02/17
TI  - Identification of Bacilli Bacteria in Acute Respiratory Infection (ARI) using Learning Vector Quantization
BT  - Proceedings of the 2nd International Conference on Social Science, Humanity and Public Health (ICOSHIP 2021)
PB  - Atlantis Press
SP  - 26
EP  - 32
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.220207.005
DO  - 10.2991/assehr.k.220207.005
ID  - Fitri2022
ER  -