• Raras Tyasnurita (1*) Institut Teknologi Sepuluh Nopember
  • Shafira Widya Hapsari (2)

  • (*) Corresponding Author
Keywords: Chronic Kidney Disease, Machine Learning, Classification, Naive Bayes, AdaBoost, Random Forest


Chronic kidney disease is a decrease in function in the kidneys where the condition leads to kidney damage. This disease causes damage to the body's immunity, because the body fails to maintain fluid balance. Therefore, it becomes a critical need to identify whether a patient is a sufferer of chronic kidney disease or not. The classification methods used in this study are Naive Bayes, AdaBoost, and Random Forest. Recently, proper early recognition is needed to detect chronic kidney disease to prevent delays in its treatment. Given the large number of chronic kidney disease cases that occur, this study is expected to be an effort to control the increase in sufferers. The results showed that the Naive Bayes approach achieved 95.4% accuracy, which increased to 98.6% after AdaBoost was implemented, and Random Forest led at 99.3%.

Author Biography

Raras Tyasnurita, Institut Teknologi Sepuluh Nopember

Information Systems Study Program Lecturer


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How to Cite
Tyasnurita, R., & Hapsari, S. W. (2020). IDENTIFICATION OF CHRONIC KIDNEY DISEASE USING NAIVE BAYES, ADABOOST, AND RANDOM FOREST LEARNING METHODS. JITK (Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), 6(1), 115-120.
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