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All Journal INTEGER: Journal of Information Technology
Khamidah, Fitri Sofia Nur
INTEGER: Journal of Information Technology
Computer Science & IT

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IMPLEMENTASI FUZZY DECISION TREE UNTUK PREDIKSI GAGAL GINJAL KRONIS Khamidah, Fitri Sofia Nur; Hapsari, Dian Puspita; Nugroho, Hendro
INTEGER: Journal of Information Technology Vol 3, No 1 (2018)
Publisher : Fakultas Teknologi Informasi Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (403.558 KB) | DOI: 10.31284/j.integer.2018.v3i1.155

Abstract

Fuzzy Decision Tree Implementation for Predicting Chronic Renal Failure. Kidney is one of the important organs for the body. The main function of kidney is for filtering process. The gradual decreasing of kidney function will lead to kidney disease and if it is left unchecked, it will lead to chronic renal failure. Chronic renal failure is a type of disease that can cause death. Until now there is no antidote for the disease of chronic renal failure, therefore this disease cannot be cured but its development can be slowed or stopped. The early diagnosis of this disease will help to prevent the fatal consequences. To diagnose the disease requires some laboratory tests in which the results of the test will be calculated and summed up by a doctor or medical practitioner. The development of science and technology, especially in the field of computers will help the doctor’s works in analyzing the results of laboratory test easier and faster. By some data as training data and implementing Fuzzy Decision Tree classification algorithm, it is expected to obtain high accuracy results that can be used as a reference for predicting chronic renal failure and avoid the occurrence of fatal consequences. The test was conducted by using some predetermined threshold and obtained the most optimal accuracy 98.28% with which indicated a fairly high level of accuracy. Thus the Fuzzy Decision Tree algorithm can be said to be able to predict the disease of chronic renal failure by the accuracy 98.28%.
Co-Authors Dian Puspita Hapsari Hendro Nugroho, Hendro