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OPTIMASI ALGORITMA RANDOM FOREST MENGGUNAKAN ALGORITMA GENETIKA DALAM PROSES KLASIFIKASI PENYAKIT GINJAL KRONIS

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Penilaian

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Chronic kidney disease is a global public health problem with a high incidence rate. About one-tenth of the world's population suffers from this disease. The high number of sufferers is accompanied by the need for proper diagnosis. Classification is one of the methods that can be used to diagnose chronic kidney disease quickly. And Random Forest is a Classification method that can produce high accuracy. This study used 40 data with 20 features consisting of blood pressure, albumin, sugar, and others. Because the data used has many features, a Genetic Algorithm is used to select these features. This study aims to optimize the Random Forest method using the Genetic Algorithm in Classification, as well as compare the performance of the Random Forest method with optimization and the Random Forest method without optimization. The test results show that the Random Forest method without being optimized produces an accuracy of 80%. While the optimized Random Forest method has greater accuracy, namely, 90% even though it requires a longer computational time and uses a lot of memory

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307000430	T87363	T873632023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T873632023

Publisher

Inderalaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xvi, 64 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem Pakar
Prodi Teknik Informatika

Specific Detail Info

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Statement of Responsibility

SEPTA

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