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Acute respiratory infections (ARI) are infectious diseases that affect both children and adults, particularly in the context of climate change. Bacteria are one of the causes of ARI. According to the government, the discovery of the bacteria that cause ARI is an indicator of successful management of infectious diseases. The current obstacle is the limited number of medical analysts, which results in longer microscopic examination times and requires a high level of objectivity. Therefore, a system for the early detection of ARI-causing bacteria was developed using digital image processing techniques, specifically channel area thresholding as one of the segmentation methods. This research employs four shape features for bacterial classification: the number of bacterial colonies, area, perimeter, and shape. The Naïve Bayes intelligent system method is used for the classification process. The system had an accuracy rate of 86.84% in the classification of four types of bacteria: S. aureus, S. pneumoniae, C. diphteriae and M. tuberculosis. [ABSTRACT FROM AUTHOR]

Acute respiratory infections (ARI) encompass diseases with epidemic prevalence and mortality (measured number of deaths in a population) worldwide. Several types of diseases are classified as acute respiratory infections (ARI), i.e., pneumonia, diphtheria, tuberculosis, and COVID-19. The Ministry of Health of Indonesia suggests that one of the performance indicators for infectious disease control and management is disease detection. Disease detection constitutes the most significant factor for infectious diseases, and early detection based on the patient's symptoms is essential to classify and treat the diseases. The delayed treatment of these diseases has resulted in many cases and deaths, especially in ARI. Furthermore, to help support the Ministry of Health's program, an expert system was established to detect early acute respiratory infections (ARI) based on the patient's symptoms. Employing a combination of forward chaining and certainty factor as the expert system method could produce a percentage range of system accuracy of 95 to 97.5%. [ABSTRACT FROM AUTHOR]