Sistem Prediksi Kasus Covid-19 di Indonesia Menggunakan Algoritma Linear Regression

Yusriyah Isnaini Mufidah, A. Saputra, Netania Indi Kusumaningtyas
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Abstract

The Coronavirus disease outbreak caused by severe acute respiratory syndrome by coronavirus 2 was first reported in Wuhan, Hubei province, China in December 2019, until March 2, 2020, President Joko Widodo announced the first case of an Indonesian citizen who was confirmed positive for COVID-19. The development of new cases of COVID-19 patients in Indonesia is still being reported even though the pandemic has lasted for almost two years. Then need a way to determine predictions or predict the number of increases in Indonesia’s COVID-19 cases in the future using machine learning technology with the Linear Regression algorithm. Estimating the number of active cases adding positive COVID-19 cases in Indonesia over the next 3 months using the machine learning method using the Linear Regression algorithm. This study predicts COVID-19 cases using machine learning with the Linear Regression algorithm. The model results have a linear coefficient, so the model predicts very well for linear data on days 0 – 300, and on the day after that, the number of positive cases of the national COVID-19 virus does not continue to show a linear relationship, the model becomes inaccurate again. The results of the parameter evaluation show that the level of accuracy is low, but this model can be used as a reference for case predictions for the next month with the results of comparison of predicted data and actual data not much different.
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使用线性回归算法的印度尼西亚 Covid-19 病例预测系统
2019年12月,中国湖北省武汉市首次报告由冠状病毒2型引起的严重急性呼吸系统综合征所导致的冠状病毒疾病疫情,直到2020年3月2日,印尼总统佐科-维多多宣布首例印尼公民确诊COVID-19阳性病例。尽管疫情已持续近两年,但印尼仍有新的 COVID-19 患者病例报告。因此需要一种方法,利用线性回归算法的机器学习技术来确定预测或预测印度尼西亚 COVID-19 病例在未来的增加数量。使用线性回归算法的机器学习方法,估算未来 3 个月印度尼西亚 COVID-19 阳性病例增加的活跃病例数。本研究利用线性回归算法的机器学习技术预测 COVID-19 病例。模型结果具有线性系数,因此模型对 0 - 300 天的线性数据预测非常准确,而在之后的日子里,全国 COVID-19 病毒阳性病例数没有继续呈现线性关系,模型又变得不准确。参数评估结果表明,该模型的准确度较低,但可以作为下一个月病例预测的参考,预测数据与实际数据的比较结果相差不大。
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