Penerapan Algoritme Support Vector Machine Terhadap Klasifikasi Tingkat Risiko Pasien Gagal Ginjal - Ratna Ayu Wijayanti; Muhammad Tanzil Furqon; Sigit Adinugroho (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2018)
Kidney failure is a condition that the kidneys can not function properly. Worldwide cases of kidney failure are on the rise every year is chronic renal failure. In Indonesia the disease sufferers of chronic kidney failure are categorized as very high. According to data from the penetri (Union of Netrologi Indonesia) was estimated at 70 thousand kidney failure chronic disease sufferers. To help knowing the status of kidney function someone, we made an intelligent system using support vector machine (SVM) algorithm for classification of risk of kidney failure and using one-againts-all strategy. The flow of research those are using correlation analysis to look at the relationships between features, with normalization for data values are at the same interval, the calculation kernel RBF, do the training process with sequential training, then use one-againts-all for the process ofclassification. This study The final test result of this research obtained the average value of accuracy is 83,998% and the highest accuracy is 98,33% using the ratio of data 80%: 20%, with the parameter value of Î» (lambda) = 1, Î³ (gamma) = 0,0001 , Ïƒ for kernel RBF = 2, C (Complexity) = 0,0001 and the number of iterations =100. Based on these results it can be concluded that the SVM algorithm and strategy one-againts-all can be used for classification of risk of kidney failure.