PERMODELAN PREDIKTIF AUTISTIC SPECTRUM DISORDER DENGAN ALGORITMA C.45
DOI:
https://doi.org/10.33480/techno.v15i2.18Kata Kunci:
AUTISTIC SPECTRUM DISORDER, C.45, C.45 algorithm, Prediction ModellingAbstrak
Autism is a developmental disability experienced throughout the life of a patient with Autistic Spectrum Disorder (ASD). The sooner it is
handled, the more likely the child will return to normal. For this reason, a new method is needed that can help parents to quickly recognize the
symptoms of autism in their children. In a previous study conducted by Fadi Fayez Tabhtah a data set was produced to detect whether a child has autism or not. But the research only produces data sets, he does not examine more in which algorithm is suitable for the data sets that have been produced. The data set attributes have some mising value, which invite a question about the accuracy of data. In this study researchers used the CRISP-DM method and test the accuracy of data sets of previous studies using the C.45 algorithm. Furthermore, the WEKA application
using feature selection and influence of the missing value for each attribute and find the most significant. These attributes are then tested with
the C.45 algorithm so that the predictive model of the data set is obtained. The A6 attribute of the decision tree calculation does not appear at all as a branch. A new model is obtained where the A6 attribute is omitted, so that when measured by the C.45 algorithm, a better accuracy value is
obtained. The results of the new model were then tested on the new questionnaire data, which produced precise predictions.
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