Polynomial Regression Method and Support Vector Machine Method for Predicting Disease Covid-19 in Indonesia

Bambang Purnomosidi Dwi Putranto, Moh. Abdul Kholik, M. A. Nugroho
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Abstract

The COVID-19 pandemic has become a major threat to the entire country. According to the WHO report, COVID-19 is a severe acute respiratory syndrome transmitted through respiratory droplets resulting from direct contact with patients. This study of data history is then processed using data mining prediction methods, namely the Polynomial Regression method compared to the Support Vector Machine method. Of the two methods will be sought the most accurate method by testing accuracy with MAE, MSE, and also MAPE to get the results of covid-19 predictions in Indonesia. Based on the comparison of test results through various scenarios against both methods, the Polynomial Regression method obtained the smallest test value, resulting in an accuracy value of MAE = 4146.025749867596, MSE = 19031800.02642069, MAPE = 0.006174164877416524. Polynomial regression is the best-recommended method
印度尼西亚Covid-19疾病预测的多项式回归方法和支持向量机方法
新冠肺炎疫情已成为全国面临的重大威胁。根据世卫组织的报告,COVID-19是一种通过与患者直接接触产生的飞沫传播的严重急性呼吸综合征。然后使用数据挖掘预测方法对数据历史进行处理,即与支持向量机方法相比的多项式回归方法。将通过使用MAE、MSE和MAPE测试准确性来寻找这两种方法中最准确的方法,以获得印度尼西亚covid-19预测结果。对比两种方法不同场景下的测试结果,多项式回归方法得到的测试值最小,准确率值MAE = 4146.025749867596, MSE = 19031800.02642069, MAPE = 0.006174164877416524。多项式回归是最好推荐的方法
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