Sistem Pakar Diagnosa Penyakit ISPA Melalui Integrasi Metode Naïve Bayes dan K-Nearest Neighbors

Abstract

Acute Respiratory Infections (ISPA) is one of the common health problems worldwide, causing serious impacts on individuals and communities. To assist in the early diagnosis of ISPA, an expert system has been developed. This system utilizes the integration of Naive Bayes and K-Nearest Neighbors (K-NN) methods to analyze the symptoms provided by users and provide accurate diagnoses. The Naive Bayes method is used to calculate the probability of each symptom for various ISPA diseases, while K-NN is used to identify patterns and relationships among the given symptoms. The integration of these two methods allows the system to leverage the strengths of each, improving accuracy and reliability of diagnosis. The system has been tested using ISPA symptom datasets, and the results demonstrate the system's ability to provide rapid and accurate diagnoses. Thus, the development of this expert system is expected to assist medical professionals in the early diagnosis of ISPA, enabling more effective treatment and preventive measures.