PERBANDINGAN KLASIFIKASI PENYAKIT DIABETES MENGGUNAKAN DECISION TREE DAN SUPPORT VECTOR MACHINE BESERTA NAÏVE BAYES
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Diabetes is a disease when the human body cannot use insulin properly. If in this case it lasts for a long time, then the glucose levels can damage the body's organs, even the failure of organ and tissue functions in the human body which can cause complications and even death. According to the International Diabetes Federation, in 2021, deaths caused by diabetes will be 236,711 thousand people aged around 20-79 years. The development of technology today, can help humans to get information and predict the disease and can help in the development of treatment and to prevent the occurrence of certain diabetes more deeply using a machine learning approach with classification techniques. The classification algorithms that will be used by the author to predict diabetes are Decision Tree Algorithm, Support Vector Machine Algorithm and Naïve Bayes Algorithm. Diabetes prediction data collected was 2768 data with each algorithm having 70% training data and 30% testing data. The algorithm that has the highest evaluation value is the Naïve Bayes algorithm with an average accuracy of 78%, precision of 77%, recall of 78%, and f1-score of 77%.
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