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Dalam penelitian ini dilakukan penyeimbangan data dengan menggunakan metode cross validation menggunakan model Random Over Sampling untuk memperoleh hasil pelatihan model, yaitu akurasi sebesar 80,93%, presisi sebesar 76,67%, sensitivitas sebesar 90,30%, dan spesifisitas sebesar 71,70%. Selanjutnya, model tersebut diuji dan memberikan hasil performa seperti berikut: akurasi sebesar 81,04%, presisi sebesar 74,22%, sensitivitas sebesar 93,85%, dan spesifisitas sebesar 68,77%. Berdasarkan data tersebut dapat disimpulkan metode Decision Tree C5.0 dapat digunakan untuk membangun model klasifikasi deteksi dini risiko serangan stroke dengan model Random Over Sampling.","\u003Cp>Stroke adalah suatu kondisi yang perlu diperhatikan karena dapat menyebabkan kecacatan dan bahkan kematian. Menurut World Health Organization (WHO) pada tahun 2022, stroke menduduki peringkat kedua sebagai penyebab kematian dan merupakan penyebab utama kecacatan di seluruh dunia. 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