<> "The repository administrator has not yet configured an RDF license."^^ . <> . . . "COMPARISON OF SUPPORT VECTOR MACHINE AND RANDOM \r\nFOREST ALGORITHMS FOR DIABETES DISEASE PREDICTION"^^ . "COMPARISON OF SUPPORT VECTOR MACHINE AND RANDOM \r\nFOREST ALGORITHMS FOR DIABETES DISEASE PREDICTION\r\nTjut Awaliyah Zuraiyah1\r\n, Halimah Tussa’diah2\r\n, Dinnar Nurhuda Hermawan3\r\n1,2,3 Department of Computer Science, Faculty of Mathematics and Natural Science, Pakuan \r\nUniversity, Bogor, West Java, 16143, Indonesia\r\nAbstract\r\nDiabetes mellitus is one of the most widespread and persistent diseases affecting humans worldwide. \r\nAround 425 million people have suffered globally today and it is estimated that up to 700 million people \r\nwill be affected by 2045. Before diagnosing this disease, doctors must analyze several factors, making \r\nthe doctor's work inefficient. However, technology can be used to make predictions or detect diabetes, \r\naccording to current advances. This technological advancement can help make it easier for doctors to \r\ntreat the disease. In the medical field, classification methods can be used to classify the severity of a \r\npatient's disease. The purpose of this study was to determine the accuracy of the support vector machine \r\nand random forest algorithm classification on the dataset of diabetes patients with 4 scenarios of dividing \r\ntrain data and test data, then a web application was created. The benefits of this study are to provide a \r\nmodel that can be used by health workers and the public for the first screening tool to identify patients \r\nwho may have diabetes. The research method used is CRISP-DM with the stages of business \r\nunderstanding, data understanding, data preparation, modeling, evaluation, and deployment. The results \r\nof this study prove that the random forest algorithm is able to predict diabetes with high performance in \r\nvarious data sharing scenarios compared to the support vector machine algorithm. The developed diabetes \r\nprediction model has the highest performance in data splitting 70:30, 75:25, and 90:10 with the following \r\nvalues: accuracy of 96%, recall of 96%, precision of 94%, and f1-score of 95%.\r\nKeywords: prediction; diabetes; CRISP-DM; random forests; support vector machines"^^ . "2024-07-14" . . . "Universitas Pakuan"^^ . . . "Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Pakuan"^^ . . . . . . . . . . . . . . . . . . "Halimah"^^ . "Tus Sadiah"^^ . "Halimah Tus Sadiah"^^ . . "Tjut"^^ . "Awaliyah Zuraiyah"^^ . "Tjut Awaliyah Zuraiyah"^^ . . "Dinnar Nurhuda"^^ . "Herman"^^ . "Dinnar Nurhuda Herman"^^ . . "Universitas Pakuan"^^ . . . "Fakultas Matematika dan Ilmu Pengetahuan Alam"^^ . . . "Program Studi Ilmu Komputer"^^ . . . . . . . "COMPARISON OF SUPPORT VECTOR MACHINE AND RANDOM \r\nFOREST ALGORITHMS FOR DIABETES DISEASE PREDICTION (Text)"^^ . . . "SKRIPSI.docx"^^ . . "HTML Summary of #8777 \n\nCOMPARISON OF SUPPORT VECTOR MACHINE AND RANDOM \nFOREST ALGORITHMS FOR DIABETES DISEASE PREDICTION\n\n" . "text/html" . . . "Ilmu Komputer"@en . .