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All Journal Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Muhammad Ramanda Hasibuan
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Pemilihan Fitur dengan Information Gain untuk Klasifikasi Penyakit Gagal Ginjal menggunakan Metode Modified K-Nearest Neighbor (MKNN) Muhammad Ramanda Hasibuan; Marji Marji
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 11 (2019): November 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Kidney has an important role in human body. Decreased kidney function can result in chronic kidney disease. Detection of chronic kidney disease using medical data that use features as factors of kidney failure, this data can be processed and build an intelligent system from it, that can help detect kidney failure. In the process of chronic kidney disease data classification method can be used. Modified K-Nearest Neighbor (MKNN) is one of classification method. But the weakness of the MKNN method is in the process using all available features, so it can cause an error detection because there are some features that are less relevant. Therefore in this research, a feature selection method is added, namely Information Gain, Information Gain method calculates the Gain value for each feature, features with large Gain values ​​will be better used for the classification process. On the results of testing variations numbers of features after selection and the effect of variations of K values, produces the highest accuracy value on 4 features with K values = 2 and 4 produces an accuracy value of 97,7% and on 6 features with K values = 2 and 4 produces an accuracy value of 97,7%. For the system testing using Information Gain produces an accuracy value of 96,8% and those not using Information Gain produce an accuracy value of 79,9%.
Co-Authors Marji Marji