The Analysis of Support Vector Machine (SVM) on Monthly Covid-19 Case Classification

Abstract

Covid-19 is disease caused by the new corona virus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The effect of this virus usually causes infection on respiratory system. Covid-19 was rapidly spread globally. Experts said that the factor that caused this to spread rapidly is human mobility. Therefore, several countries create new rules so that it can suppress the spreading of this disease, by prohibiting a large scale gathering, keeping away distance with each other, mandatory rule of using mask, and the prohibition for the entry of their country. This research proposes a performance analysis of Support Vector Machine (SVM) to classify the monthly data of covid-19. The data used in this research is a series of covid-19 data of towns in Bandung from November 2020 until December 2021. From conducting this research It is found that the best accuracy was found on December 2021 with the accuracy of 100%, followed by July and August with the accuracy of 97%, and October with the value of 90%. We can conclude that Support Vector Machine (SVM), is a good method on classifying the monthly covid-19 data.

Optimizing Machine Learning for Daily Rainfall Prediction in Bogor City, Indonesia: A Two-Stage Statistical Downscaling Approach

“…The Support Vector Classi cation (SVC) emerges as an effective tool for managing both linear and nonlinear data types, particularly in classifying rainy and non-rainy days within non-linear time series data [11]. The SVC model, adept at recognizing patterns and delivering accurate results, optimizes its hyperplane by maximizing the distance between groups [12]. However, challenges arise when linear separation in the input space is unfeasible, necessitating the use of kernels to transform data into a space of grater dimensionality [13].…”