The The Classification of Acute Respiratory Infection (ARI) Bacteria Based on K-Nearest Neighbor
Acute Respiratory Infection (ARI) is an infectious disease. One of the performance indicators of infectious disease control and handling programs is disease discovery. However, the problem that often occurs is the limited number of medical analysts, the number of patients, and the experience of medical analysts in identifying bacterial processes so that the examination is relatively longer. Based on these problems, an automatic and accurate classification system of bacteria that causes Acute Respiratory Infection (ARI) was created. The research process is preprocessing images (color conversion and contrast stretching), segmentation, feature extraction, and KNN classification. The parameters used are bacterial count, area, perimeter, and shape factor. The best training data and test data comparison is 90%: 10% of 480 data. The KNN classification method is very good for classifying bacteria. The highest level of accuracy is 91.67%, precision is 92.4%, and recall is 91.7% with three variations of K values, namely K = 3, K = 5, and K = 7.
 S. J. Pitt, Clinical Microbiology for Diagnostic Laboratory Scientists. Chichester, UK: John Wiley & Sons, Ltd, 2017. doi: 10.1002/9781118745847.
 K. Struthers, Clinical Microbiology, Second Edi. New York: CRC Press, 2017.
 C. R. Mahon and D. C. Lehman, Textbook of Diagnostic Microbiology, Sixth Edit. St. Louis, Missouri: Elsevier, 2019. doi: 10.1309/u0mb-0p7r-rrwf-4bth.
 Dinas Kesehatan Provinsi Jawa Timur, Profil Kesehatan Provinsi Jawa Timur 2019. Surabaya: Dinas Kesehatan Provinsi Jawa Timur, 2020.
 R. Yuliwardana, “Deteksi Bakteri Streptococcus pneumoniae Berbasis Jaringan Syaraf Tiruan dari Citra Mikroskop Digital,” Universitas Airlangga, Surabaya, 2016.
 R. Rulaningtyas, Andriyan Bayu Suksmono, T. Mengko, and P. Saptawati, "Multi patch approach in K-means clustering method for color image segmentation in pulmonary tuberculosis identification," in 2015 4th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), Bandung, Indonesia, Nov. 2015, pp. 75–78. doi: 10.1109/ICICI-BME.2015.7401338.
 K. S. Mithra and W. R. S. Emmanuel, "Segmentation of Mycobacterium Tuberculosis Bacterium From ZN Stained Microscopic Sputum Images," in 2018 International Conference on Smart Systems and Inventive Technology (ICSSIT), Tirunelveli, India, Dec. 2018, pp. 150–154. doi: 10.1109/ICSSIT.2018.8748294.
 L. N. Sahenda, M. H. Purnomo, I. K. E. Purnama, and I. D. G. H. Wisana, "Comparison of Tuberculosis Bacteria Classification from Digital Image of Sputum Smears," in 2018 International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM), Surabaya, Indonesia, Nov. 2018, pp. 20–24. doi: 10.1109/CENIM.2018.8711386.
 F. H. Kayser, Ed., Medical microbiology. Stuttgart ; New York, NY: Georg Thieme Verlag, 2005.
 P. R. Murray, Basic Medical Microbiology. Philadelphia: Elsevier, 2018.
 Z. E. Fitri, A. Baskara, M. Silvia, A. Madjid, and A. M. N. Imron, "Application of backpropagation method for quality sorting classification system on white dragon fruit (Hylocereus undatus)," IOP Conference Series : Earth Environmental Science, vol. 672, no. 1, p. 012085, Mar. 2021, doi: 10.1088/1755-1315/672/1/012085.
 A. M. Nanda Imron and Z. E. Fitri, "A Classification of Platelets in Peripheral Blood Smear Image as an Early Detection of Myeloproliferative Syndrome Using Gray Level Co-Occurrence Matrix," Journal of Physics: Conference Series, vol. 1201, p. 012049, May 2019, doi: 10.1088/1742-6596/1201/1/012049.
 Z. E. Fitri, U. Nuhanatika, A. Madjid, and A. M. N. Imron, “Penentuan Tingkat Kematangan Cabe Rawit (Capsicum frutescens L.) Berdasarkan Gray Level Co-Occurrence Matrix,” Jurnal Teknologi Informasi Dan Terapan (JTIT), vol. 7, no. 1, pp. 1–5, Jun. 2020, doi: 10.25047/jtit.v7i1.121.
 Z. E. Fitri, R. Rizkiyah, A. Madjid, and A. M. N. Imron, “Penerapan Neural Network untuk Klasifkasi Kerusakan Mutu Tomat,” Jurnal Rekayasa Elektrika, vol. 16, no. 1, May 2020, doi: 10.17529/jre.v16i1.15535.
 Z. E. Fitri, L. N. Y. Syahputri, and M. N. Imron, "Classification of White Blood Cell Abnormalities for Early Detection of Myeloproliferative Neoplasms Syndrome Based on K-Nearest Neighbor," Scientific Journal of Informatics, vol. 7, no. 1, p. 7, 2020.
 I. M. A. S. Widiatmika, I. N. Piarsa, and A. F. Syafiandini, "Recognition of The Baby Footprint Characteristics Using Wavelet Method and K-Nearest Neighbor (K-NN)," Lontar Komputer Jurnal Ilmiah Teknologi Informasi, vol. 12, no. 1, p. 41, Mar. 2021, doi: 10.24843/LKJITI.2021.v12.i01.p05.
 R. J. Al Kautsar, F. Utaminingrum, and A. S. Budi, “Helmet Monitoring System using Hough Circle and HOG based on KNN,” Lontar Komputer Jurnal Ilmiah Teknologi Informasi, vol. 12, no. 1, p. 13, Mar. 2021, doi: 10.24843/LKJITI.2021.v12.i01.p02.
The Authors submitting a manuscript do so on the understanding that if accepted for publication, copyright of the article shall be assigned to Jurnal Lontar Komputer as publisher of the journal. Copyright encompasses exclusive rights to reproduce and deliver the article in all form and media, as well as translations. The reproduction of any part of this journal (printed or online) will be allowed only with a written permission from Jurnal Lontar Komputer. The Editorial Board of Jurnal Lontar Komputer make every effort to ensure that no wrong or misleading data, opinions or statements be published in the journal.
This work is licensed under a Creative Commons Attribution 4.0 International License.