Arsy, Nurul (2025) Klasifikasi Penentuan Penyakit Infeksi Saluran Pernapasan Akut Berdasarkan Gejala Menggunakan Algoritma NaÏve Bayes. Other thesis, Universitas Islam Riau.

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Abstract

Acute respiratory infections (ARI) are diseases of the upper and lower respiratory tract that can affect children to the elderly usually caused by microorganisms that affect one or more parts of the human respiratory tract, ranging from the nose to the esophagus (upper part) and larynx trachea to bronchi (lower part) which are usually contagious and can cause death depending on the causative pathogen. Manual recording of patient complaints is considered less efficient in today's computational age. This study aims to assist medical personnel in determining the type of ARI disease based on the symptoms experienced by patients so that patients get the right treatment, so a classification of ARI disease determination is needed. This system was built using the python programming language, jupyter notebook and streamlit framework. The method used in this research is Naïve Bayes with 9 (nine) attributes used, namely fever, cough, runny nose, sore throat, nausea, no appetite, vomiting, shortness of breath and wheezing. Model testing using confusion matrix on ARI disease classification with 688 training data and 173 test data resulted in an accuracy value of 96.53%, precision 93.18%, recall 86.67% and F1-score 89.81%. From the test results conducted, it shows that the method can be used in the classification of ARI diseases.

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