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All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI
Dewantara, Moreno
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Computer Science & IT

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Analisis Komparatif Algoritma KNN dan SVM dalam Klasifikasi Tingkat Keparahan COVID-19 Global Syaputra, Rifky Achmad; Dewantara, Moreno; Dwi Putra, Dimas Arya
JATISI Vol 12 No 4 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i4.13806

Abstract

The severity of COVID-19 varies in each country, requiring an analytical approach that can provide accurate classification as a basis for global health decision-making. Machine learning methods are an effective option for detailing the severity based on data patterns regarding cases, deaths, and other indicators. In this study, the K-Nearest Neighbor (KNN) algorithm was compared with Support Vector Machine (SVM) using a global dataset on COVID-19 taken from Kaggle. The analysis process included data pre-processing, data exploration, model building, and evaluation using accuracy, precision, recall, and F1-score metrics. The results of the evaluation showed that SVM performed better with an accuracy of 87%, while KNN only reached 78%. In addition, SVM also produced a lower and more consistent classification error rate in each severity category. Based on these findings, SVM is considered more efficient in classifying the severity of COVID-19 in globally distributed data that is unevenly distributed.
Co-Authors Dwi Putra, Dimas Arya Syaputra, Rifky Achmad