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Submitted
17 June 2023
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21 June 2023
Published
28 June 2023
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Abstract

Corona Virus Disease 2019 (covid-19) merupakan pandemi dunia yang menimbulkan berbagai kerugian di dunia. Diagnosis covid-19 yang valid memerlukan waktu yang cukup lama dan hasil ini tidak sepenuhnya akurat. Salah satu cara untuk meningkatkan hasil akurasi adalah dengan menggunakan image classification. k-Nearest Neighbor (kNN) adalah salah satu Teknik klasifikasi yang paling banyak digunakan untuk melakukan pekerjaan tersebut, hanya saja kNN masih memiliki kelemahan. Untuk mengatasi kelemahan pada kNN, maka dilakukan modifikasi dengan menambahkan local mean dan distance weight, di mana varian kNN ini dikenal dengan nama Local Mean Distance Weight k-Nearest Neighbor (LMDWkNN). Oleh sebab itu, penelitian kali mencoba membandingkan kinerja kedua algoritma ini untuk melakukan image classification pada citra covid-19. Kinerja diukur berdasarkan nilai akurasi, precision, dan recall, di mana dari hasil pengujian terbukti bahwa kinerja LMDWkNN lebih baik dari pada kinerja kNN. Algoritma LMDWkNN mengalami peningkatan rata-rata sebesar 3.5% untuk nilai akurasi, 2.89% untuk precision, dan 3.54% untuk recall. Meskipun begitu kNN tetap mampu menghasilkan kinerja yang sama baiknya yang mana kinerja kNN akan sangat bergantung dari nilai k yang digunakan

Keywords

Corona Virus Disease 2019 (covid-19) Image Classification k-Nearest Neighbor (kNN) Local Mean Distance Weight k-Nearest Neighbor (LMDWkNN) Performance

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Author Biography

Sapriadi Sapriadi, institut kesehatan helvetia

Farmasi, Fakultas Farmasi dan Kesehatan, Institut Kesehatan Helvetia, Medan
How to Cite
Sapriadi, S. (2023). Perbandingan Kinerja k-Nearest Neighbor dan Local Mean Distance k-Nearest Neighbor Pada Data Citra Covid-19. Jurnal Komputer Terapan, 9(1), 74–81. https://doi.org/10.35143/jkt.v9i1.6019
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