THE EFFECT OF USING DUMMY VARIABLE ON CLASSIFICATION OF WOMB DISEASE WITH C4.5 METHOD
Abstract: The use of dummy
variables is recommended because the symptoms of the womb disease compounds
that have the possible values that appear more than two (non-binary), there is
a possibility that not all types of occurrence related to the disease symptoms
as other content that needs to be done solving the symptoms so that the value
to binary and symptoms become more specific. By applying the dummy variable, is
expected to improve the accuracy of the probabilistic approach Naïve Bayes
classifier, because the assumption of independency between the symptoms of the
disease are met. Besides Naïve Bayes classifier, Decission Tree is also
commonly used in classification, one of Decission Tree method is C4.5. This
study discusses the effect of the use of dummy variables in the womb disease
classification using C4.5. From the results of this study concluded that the
use of dummy variables to produce an average value accuracy, precission,
recall, and F-measure which remained stable at 87.2% in testing k-fold cross
validation with value of k (5, 10, 15,
20, and 25). However, the use of dummy variables reduces the average value of
accuracy, precission, recall, and F-measure sequentially from 89.6%, 89.74%,
89.7%, and 89.6% to 87.2%, 87.2%, 87.2% and 87.2%. Besides, the use of dummy
variables to specify the attributes of disease symptoms used in the
classification of disease womb.
Penulis: Moch Shofieyuddin
Kode Jurnal: jptinformatikadd160504
