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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Azizul Hanifah Hadi
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Identifikasi Penyakit Gagal Ginjal Menggunakan Metode Neighbor Weighted K-Nearest Neighbor (NWKNN) Azizul Hanifah Hadi; Dian Eka Ratnawati; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 9 (2018): September 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Kidney disease can be caused by several factors such as hypertension, uric acid levels, creatinine levels, diabetes, and many others. From that factors, we know about the level of kidney disease risk. Some people are unaware, lazy and indifferent about health, especially on kidney disease because of the long process and complicated. According to the Indonesian Renal Registry, in 2014 patients with kidney disease in Indonesia reach 12,770 inhabitants. Therefore, we need a system that can detect or identify the kidney disease. In this research, we will identify the kidney disease using Neighbor Weighted K-Nearest Neighbor (NWKNN) method. This method is similar to the KNN method but the differentiates are in the weighting process in each identification class. Identification class in this study decided in two part, ckd or exposed to kidney disease and notckd or not affected kidney disease. The results of this study indicate that the NWKNN method can identify kidney disease when the data are 150 data and the test data are 50 data with K = 2 and E = 2 and accuracy level is 88%.
Co-Authors Candra Dewi Dian Eka Ratnawati