Open AccessPenerapan Decision Tree Menggunakan Algoritma C4.5 Untuk Deteksi Demam Berdarah Pada RS. IMC Bintaro
Author(s) -
Farid Fadli,
Belsana Butar Butar
Publication year - 2019
Publication title -
indonesian journal on software engineering (ijse)/ijse (indonesian journal on software engineering)
Language(s) - English
Resource type - Journals
eISSN - 2714-9935
pISSN - 2461-0690
DOI - 10.31294/ijse.v5i1.5866
Subject(s) - decision tree , tree (set theory) , computer science , artificial intelligence , data mining , statistics , pattern recognition (psychology) , mathematics , combinatorics
According to the WHO report in 2004, Indonesia is the largest country with the highest number of sufferers and death rates due to dengue fever. If it is not handled properly, the postponed treatment can be fatal. In this study, the authors used the kepuutsan tree method with C4.5 algorithm to process patient data to predict whether patients experienced bloody help regarding existing indications with the help of Rapidminer software. The results of data processing using Rapidminer were evaluated and validated with a confussion matrix and AUC curve, the results of data processing using the C4.5 algorithm had an accuracy of 72% and AUC had a value of 0.758 with a fair classification category.
Keywords: Algorithm C4.5, Decision Tree, Data Mining