IMPUTASI MISSING ATTRIBUTE VALUES DATASET HEPATITIS BERDASARKAN ALGORITME RIPPER - Tri Astuti; Yuliyanti Yuliyanti (Telematika, 2015)
Hepatitis is a liver disease which caused by a hepatitis virus. Nowdays hepatitis is a global health problems, including in Indonesia. Chronic hepatitis can lead to cirrhosis and liver cancer, therefore early diagnosis is needed. The diagnosis process of hepatitis disease are done through computer aided method using hepatitis dataset nowdays. University California Irvine (UCI) machine learning repository has been providing hepatitis disease dataset which can be accessed to public but the dataset contains many missing values. The existing of missing values in the dataset may affect the quality of the analysis results, therefore it needs to be conducted for handling the missing values. Imputation method based on machine learning is one of the methods to handle the missing value. The aims of this research is to develop the imputation methods of missing value using machine learning algorithm based on RIPPER on hepatitis dataset. Result shows that the imputation method based on RIPPER achives 87,50% accuracy for hepatitis dataset. It is expected that the developed method can contribute for helping the clinicans and practicians by providing imputed hepatitis dataset in diagnosing the hepatitis disease.