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Medha, Ied Andro
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Computer Science & IT

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IMPLEMENTASI FUZZY DECISION TREE UNTUK PREDIKSI PENYAKIT LIVER PADA DATASET ILPD (INDIAN LIVER PATIENT DATASET) Medha, Ied Andro; Sari, Dian Puspita
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 5 No 2 (2019): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v5i2.773

Abstract

Human liver belongs to one of the main body organs which functions for changing poisonous to be nutritious substances in order to control the hormone level of body. Consequently, liver disease can cause many problems in the human life. Fast and accurate prediction on it enables early and effective treatment. Nowadays, the advancement of computer technology has eased the diagnosis or decision-making process. Machine learning allows a computer to decide or give right suggestion. Trial on it has been carried out by fuzzy decision tree algorithm with some threshold values namely fuzziness control threshold (𝜃𝑟) and leaf decision threshold (𝜃𝑛) which were determined trial method of 10-fold cross validation and obtained accuracy by 78.95%. Since this accuracy was gained from distinct threshold values, the researcher used Receiver Operating Characteristics (ROC) method to investigate the performance of good accuracy. The result of ROC demonstrated that the most optimum accuracy by 78.95% was obtained on 𝜃𝑟=75%,77%,80%,82%,85%,87%,90%,92%,95%,98% and 𝜃𝑛=6%.Keywords: liver disease, Fuzzy Decision Tree, K-Fold Cross Validation, Receiver Operating Characteristics
Co-Authors Dian Puspita Sari