The The Classification of Acute Respiratory Infection (ARI) Bacteria Based on K-Nearest Neighbor
Zilvanhisna Emka Fitri, Lalitya Nindita Sahenda, Pramuditha Shinta Dewi Puspitasari, Prawidya Destarianto, Dyah Laksito Rukmi, Arizal Mujibtamala Nanda Imron
Abstract
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.