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Ega Ajie Kurnianto
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Klasifikasi Penderita Penyakit Ginjal Kronis Menggunakan Algoritme Support Vector Machine (SVM) Ega Ajie Kurnianto; Imam Cholissodin; Edy Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Data mining is one of the processes that can be used in the healthcare industry currently. With the large amount of data collected, it can be used to get some information or an interesting pattern. Later on, the information can be used to provide assistance, diagnose, or decision making of a patient with the certain disease, such as chronic kidney disease, which is one form of disorder in the kidney. It is a deadly disease, but with proper precautions, this disease can also be avoided. Usually, most patients with a chronic kidney disease don't know the suffered disease and patients tend to underestimate when they find early symptoms of chronic kidney disease. Therefore, it needs a system that can facilitate the early detection of the chronic kidney disease. One technique that can be used is the classification using Support Vector Machine (SVM) algorithm. This algorithm aims to create an optimal hyperplane or dividing line. This research used data from 158 patients with 24 features and 2 classes. Based on test results, obtained best accuracy 100% with the details of parameter value is augmenting factor value (λ) = 0,001, learning rate value (γ) = 0,001, complexity value (C) = 0,001, sigma value (σ) = 1, and number of iteration = 1000.
Co-Authors Edy Santoso Imam Cholissodin