PERBANDINGAN HASIL OPTIMASI ALGORITMA EXTREME LEARNING MACHINE MENGGUNAKAN ALGORITMA PARTICLE SWARM OPTIMIZATION DAN ALGORITMA GENETIKA UNTUK MENGKLASIFIKASI PENYAKIT GINJAL KRONIS

Albani Kautsar, . (2023) PERBANDINGAN HASIL OPTIMASI ALGORITMA EXTREME LEARNING MACHINE MENGGUNAKAN ALGORITMA PARTICLE SWARM OPTIMIZATION DAN ALGORITMA GENETIKA UNTUK MENGKLASIFIKASI PENYAKIT GINJAL KRONIS. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

Chronic Kidney Disease (CKD) is a condition where there is a gradual and severe decline in kidney function caused by various kidney diseases. This disease is progressive and generally irreversible. To address this issue, a fast and accurate method is needed to diagnose chronic kidney disease promptly for effective treatment. One appropriate method to predict CKD diagnosis is by building a classification model using various algorithms, including Extreme Learning Machine (ELM). The use of the ELM algorithm is beneficial for classification cases as it can classify rapidly and produce good results. To optimize the accuracy of the ELM algorithm, feature selection is employed. This study utilizes Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for feature selection. Seventeen features are selected using PSO, while eleven features are selected using GA. However, in the final results, both feature selection algorithms lead to decreased accuracy. The PSO feature selection yields an accuracy of 88%, and the GA feature selection yields an accuracy of 87%, whereas the classification using the ELM algorithm results in an accuracy of 99.7%.

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil: 1910511047] [Pembimbing: Nur Hafifah Matondang] [Penguji 1: Yuni Widiastiwi] [Penguji 2: Helena Nurramdhani Irmanda]
Uncontrolled Keywords: Chronic Kidney Disease, extreme learning machine, particle swarm optimization, genetic algorithm
Subjects: Q Science > Q Science (General)
T Technology > T Technology (General)
Divisions: Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User: Albani Kautsar
Date Deposited: 26 Jul 2023 03:41
Last Modified: 26 Jul 2023 03:41
URI: http://repository.upnvj.ac.id/id/eprint/26106

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