Klasifikasi Penyakit Batu Ginjal Menggunakan Algoritma Decision Tree C4.5 Dengan Membandingkan Hasil Uji Akurasi

  • Yuni Widiastiwi
  • Iin Ernawati
Keywords: Kidney Stones, Classification, Decision Tree, Accuracy


The decision tree is one of the classification algorithms that can use to identify factors that cause an event, one of which is kidney stone disease. Kidney stones are one of the most common causes of urinary tract disorders. The formation of kidney stones commonly influenced by intrinsic and extrinsic factors. Intrinsic factors are factors that come from within the individual, namely age, gender, heredity, or family history. Extrinsic factors are factors that come from the environment outside the individual like drinking and eating habits. The aim of This study conducted to classify and compare the results of the accuracy-test against a dataset of kidney stone disease medical records. The research method approach used is to use the decision tree C4.5. To classify and get the best accuracy results with the training data and test data with three categories. The results expected in this study are in the form of decision tree formation information also the best accuracy test results using training data as much as 70% produces an accuracy rate of 95,71%.