# PREDIKSI JUMLAH PASIEN COVID-19 DENGAN MENGGUNAKAN KLASIFIKASI ALGORITMA MACHINE LEARNING

Amelia, Putri Juli (2023) PREDIKSI JUMLAH PASIEN COVID-19 DENGAN MENGGUNAKAN KLASIFIKASI ALGORITMA MACHINE LEARNING. Undergraduate thesis, Universitas Muhammadiyah Malang.

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## Abstract

Coronavirus or Servere Acute Repository Syndrome Coronavirus 2 (SARS-Cov-2) is a disease that results in mild to moderate respiratory infections. A positive case of Covid-19 in Indonesia was first detected on March 2, 2020 and continues today. The increase in the number of deaths caused by COVID-19 has also increased. Advances in machine learning methods make it possible to assist the government in predicting the number of deaths caused by COVID-19 with it the government can provide appropriate treatment so that these COVI-19 cases can be quickly resolved. Therefore, this study is how to predict the number of patients who die from COVID-19 in Indonesia by making an appropriate accuracy model to help estimate the number of deaths associated with COVID-19 in Indonesia. In this study, the authors used the Decision Tree model using the Entropy criteria as well as Information Gain and Random Forest which resulted in an accuracy rate of 75.07% (Decision Tree) and 93.44% (Random Forest). These results explain that the model used is good enough to make a prediction. The more the R-squared error value approaches the value of 1, the better the model used.

Item Type: Thesis (Undergraduate) 201910370311256 Covid-19, Machine Learning, Classification L Education > L Education (General)Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4450 Databases Faculty of Engineering > Department of Informatics (55201) 201910370311256 putrijuliaml 21 Nov 2023 02:26 21 Nov 2023 02:26 https://eprints.umm.ac.id/id/eprint/1157

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