Reduksi Atribut Pada Dataset Penyakit Jantung dan Klasifikasi Menggunakan Algoritma C5.0

 (*)Dito Putro Utomo Mail (STMIK Mikroskil, Medan, Indonesia)
 Pahala Sirait (STMIK Mikroskil, Medan, Indonesia)
 Roni Yunis (STMIK Mikroskil, Medan, Indonesia)

(*) Corresponding Author

Submitted: August 21, 2020; Published: October 20, 2020



Coronary heart disease, commonly referred to as cardiovascular, heart disease is a disease with a high mortality rate. Thus diagnosis is very important and is an important area of medical research. In the diagnostic process, the most frequently encountered problems are time in making decisions and the lack of accuracy in the classification process. Attributes are important in making decisions on heart disease so it is necessary to know the main attributes of heart disease. Often different results are obtained in the diagnostic process due to the many attributes used in decision making. So it is necessary to do a reduction process in the attributes of heart disease. Principal Component Analysis (PCA) method can be used for data reduction with large dimensions and ranking the attributes to be reduced. The classification process can be done using the C5.0 Algorithm and getting a level of accuracy in the classification process. The results obtained in this study reduce the 12 attributes of the heart disease dataset and classify them with a combination of attributes after the reduction process is carried out. The results obtained with the highest level of accuracy when classifying with 11 attribute combinations where there is 1 attribute that is reduced, the accuracy rate obtained is 89.11%.


Data Mining, Reduction, Heart Disease, Principal Compnent Analysis, C5.0.

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