PENERAPAN ALGORITMA C4.5 UNTUK MEMBUAT MODEL PREDIKSI PASIEN YANG MENGIDAP PENYAKIT DIABETES
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Abstract
Diabetes is a disease in which the pancreas cannot produce insulin properly. Insulin is a hormone produced from the pancreas, which is useful as a door to channel glucose from food that is absorbed to be flowed into blood cells so that the body can produce energy. Meanwhile, according to WHO diabetes is a very deadly disease which is ranked 9th in the world. People with diabetes generally die from damage to several vital organs such as the heart, kidneys and liver. The early sufferers of diabetes are not known with certainty so that diabetics who have been treated are in a worrying condition. To reduce the number of deaths due to diabetes, a system is needed that can identify early symptoms of diabetes, so that people with diabetes can be handled properly. Data mining technology can help build a system to predict diabetes using the C4.5 decision tree algorithm. In this study, diabetes prediction data was taken from the UCI Repository. Then the data is processed in stages, select data, pre-processing and split validation using rapid miner. The results of data processing using a rapid miner, in the form of rules that can be used to predict diabetes. The rules generated from the RapidMiner decision tree have an accuracy of 95.51%.
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