Perbandingan Rule Decision Tree c4.5 Dengan Data Input Tidak Lengkap Dan Prediksi Missing Imputation Pada Data Penyakit Ginjal Kronis

Kursita Dewi, Safitri (2020) Perbandingan Rule Decision Tree c4.5 Dengan Data Input Tidak Lengkap Dan Prediksi Missing Imputation Pada Data Penyakit Ginjal Kronis. Undergraduate thesis, Politeknik Negeri Jember.

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Abstract

In the classification process, the problem that occurs in a study is the loss of the value of an attribute called missing value. Missing value is the loss of value in the dataset or the absence of a value for certain attributes. The cause of missing value is human error such as negligence in data collection, errors during data entry, and the inability of respondents to provide accurate answers. This study conducted trials on the development of rule decision tree C4.5 for chronic kidney disease data. In chronic kidney disease data, there are incomplete data and predict missing imputation. By conducting trials on the development of the C4.5 rule decision tree, it can be seen the difference in the results of the rules and the accuracy obtained. This study uses 360 training data and 40 test data. The resulting rules for incomplete data were 21 and predicted data were 24. Accuracy for incomplete data was 90% and predicted data was 95%.

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