W.F.A. Siahaan, O.S. Sitompul, Z. Situmorang
Classification is an important technique in data mining to create patterns and data modeling. Hepatitis is a disease that is dangerous to humans as this disease affects the human liver which is a vital organ. Early detection of hepatitis by diagnosing its occurrence on patients is needed to give them help, care, and treatment. Therefore, a classification technique is required to create patterns and models of data. To achieve accuracy in classifying hepatitis there would be requirements to separate relevant from irrelevant features. By only considering relevance features in classifying hepatitis, it is expected that the classification process will be faster, easier, and more accurate. In this research, the ANFIS algorithm and genetic algorithm are used in classifying the diagnosis of hepatitis based on a dataset from the UCI data mining repository. Results obtained showed that the genetic algorithm gave a slightly higher accuracy of 98.73% compared to 86.67% results obtained using ANFIS. © Published under licence by IOP Publishing Ltd.
Graduate Program of Computer Science, Universitas Sumatera Utara, Medan, Indonesia; Department of Information Technology, Universitas Sumatera Utara, Medan, Indonesia; Department of Computer Science, Universitas Katolik Santo Thomas, Medan, Indonesia