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Background and Aims Chronic kidney disease is one type of disease that can cause death. Until now, chronic kidney failure has no antidote, so this disease cannot be cured but can be slowed down or stopped its development. Early diagnosis of this disease will help to prevent these fatal consequences. To diagnose this disease, several laboratory tests are needed in which the results of these tests will be calculated and concluded the results by a doctor or medical practitioner. The development of science and technology, especially in the field of computers will help the work of doctors to analyze the results of laboratory tests become easier and faster. In this study, a prediction attempt is made using the Fuzzy Decision Tree classification algorithm, which is expected to obtain high accuracy results. Method This study uses the Chronic Kidney Disease (CKD) dataset taken from the UCI Machine Learning Repository. Data was collected from the hospital for approximately two months. This dataset covers a total of 400 samples with numerical attributes totaling 11 columns and nominal totaling 14 columns. Data samples were provided as many as 400 rows with 250 samples being the ckd group (positive for chronic kidney failure) and 150 samples for the notckd group (chronic kidney failure). But after going through the preprocessing stage, data that can be used amounted to 158 rows with 43 samples are the ckd group (positive chronic kidney failure) and 115 samples of the notckd group (negative chronic kidney failure). Results The trial was conducted using several predetermined thresholds and the most optimal accuracy was 98.3%, which showed a fairly high degree of accuracy. Conclusion Thus, it can be concluded that the Fuzzy Decision Tree algorithm can be said to be able to predict chronic kidney failure with a very good results.

P1835PREDICTION OF CHRONIC OF KIDNEY FAILURE BASED ON ARTIFICIAL INTELLIGENCE SYSTEM USING THE FUZZY DECISION TREE ALGORITHM