Analisis Perbandingan Algoritma Supervised Learning untuk Prediksi Kasus Covid-19 di Jakarta

Angeline Septhiani(1*), H Hendry(2),

(1) Universitas Kristen Satya Wacana, Indonseia
(2) Universitas Kristen Satya Wacana, Indonseia
(*) Corresponding Author

Abstract


Coronavirus disease or called COVID-19 is a pandemic according to World Health Organization (WHO) in February. The virus gives several symptoms, such as cough, asthma, and fever. The data and information are the important part of making a good decision. Those data need to be processed and analyzed to be useful information. In this research, the data will be used to predict the COVID-19 issue in Jakarta, using several supervised learning algorithm models, such as K-Nearest Neighbors, Neural Network, Linear Regression, Support Vector Machine, and Random Forest. Using 10 Fold Cross Validation in model testing and T-Test to get the model with the best accuracy. According to this research, the algorithm that has the best accuracy is K-Nearest Neighbors with the lowest RMSE, 1096.188 +/- 365.077 (micro average: 1149.601 +/- 0.000).

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References


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DOI: http://dx.doi.org/10.30645/j-sakti.v7i2.668

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