<![CDATA[Diagnosing a disease using technology is no longer commonplace, with the continuous development of advances in the world of health, it can utilize technology in making decisions, especially detecting liver disease. Basically, the technology used can really help doctors compared to conventional manual analysis techniques which have been used to diagnose patient diseases. According to WHO (Word Heart Organization) in 2013, there were 28 million patients with liver disease in Indonesia. From this data, liver disease is referred to as one of the 10 diseases with the highest death rate. It would be very good if doctors could detect liver disease more frequently. quickly so that the patient can be immediately treated by a doctor. From the problems above that underlies the authors to conduct research on the classification of liver disease. In this study the authors wanted to predict liver disease using the Random Forest Algorithm. In selecting the right features and classifiers, the most important thing is to increase accuracy and computation in predicting liver disease. The researcher wants to know whether the Random Forest algorithm has a high accuracy value so that it can be a basis for using the Random Forest algorithm in predicting liver disease. Researchers used the Liver Disease Patient Dataset, in this research stage several steps were carried out starting from conducting Data Analysis, Exploratory Data Analysis, Preprocessing, Algorithmic Modeling, and Visualization. From this research stage, it can be seen the results of predicting accuracy using the Random Forest Algorithm. From the results of research conducted with the Random Forest algorithm, predictions were obtained with an accuracy value of 0.713326 with an f1 score of 81%.]]>
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