Penerapan Seleksi Fitur pada Deteksi Coronavirus Disease 19 (COVID-19) berbasis Random Forest

Penulis

DOI:

https://doi.org/10.32493/jtsi.v7i4.40755

Kata Kunci:

Seleksi Fitur; Prediksi; COVID-19

Abstrak

Data Mining menggunakan algoritma pembelajaran mesin (machine learning) dapat digunakan untuk membantu menganalisis data historis untuk memprediksi COVID-19. Dataset yang digunakan untuk memprediksi COVID-19 memiliki banyak fitur, namun fitur tersebut memiliki kemungkinan redundansi atau tidak relevan yang dapat menyebabkan penurunan kinerja pengklasifikasi. Penelitian ini mengusulkan model yang menerapkan pemilihan fitur (feature selection) untuk memilih fitur yang relevan dan dapat memberikan prediksi kinerja yang lebih baik untuk diagnosa/prediksi COVID-19. Beberapa teknik pemilihan fitur yang diusulkan adalah Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Sequential Forward Floating Selection ( SFFS), Sequential Forward Floating Selection (SBFS), Sequential Backward Floating Selection (SBFS), dan selectKBest. Algoritma klasifikasi yang digunakan untuk mengklasifikasikan adalah Random Forest. Model yang memberikan nilai kinerja terbaik adalah model yang menerapkan SFS dan SFFS sebagai seleksi fitur.

Biografi Penulis

Aries Saifudin, Universitas Pamulang

Received A.Md. (Associate Degree) in Electronic Engineering from Polytechnic of Brawijaya University, Malang, S.T. (Bachelor Degree) in Informatics Engineering from Mercu Buana University, Jakarta, and M.Kom (Master Degree) in Software Engineering from STMIK ERESHA, Jakarta. He is a lecturer at Informatics Engineering, Pamulang University. His current research interests include software engineering, intelligent systems, and machine learning.

Publication:

SCOPUS ID: 57212083789

Researcher ID: AAJ-9700-2021

SINTA ID: 256981

Google Scholar ID: rN9QTUgAAAAJ

ORCID ID: 0000-0002-3882-633X

Garuda ID: 459608

 

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Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 4 Oktober 2024

Unduhan

Diterbitkan

2024-10-31

Cara Mengutip

Saifudin, A., Nirmala, E., & Kusyadi, I. (2024). Penerapan Seleksi Fitur pada Deteksi Coronavirus Disease 19 (COVID-19) berbasis Random Forest. Jurnal Teknologi Sistem Informasi Dan Aplikasi, 7(4), 1548–1556. https://doi.org/10.32493/jtsi.v7i4.40755

Terbitan

Vol 7 No 4 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi

Bagian

Article

Lisensi

Hak Cipta (c) 2024 Aries Saifudin, Endar Nirmala, Irpan Kusyadi

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