Prediction of Covid-19 Infection in Indonesia Using Machine Learning Methods

HASNA ZAKIYYAH

Informasi Dasar

21.04.773
006.31
Karya Ilmiah - Skripsi (S1) - Reference

Currently, the world is experiencing a prolonged pandemic known as Covid-19. Many prediction models of Covid-19 have been developed by the governments to make the right decisions to control the outbreak. In Indonesia, there is also much research on the prediction of Covid-19 using machine learning methods, which provide the statistics to predict the total cases, the total deaths, the peak and the end of the pandemic. This paper investigates three prediction models: Gaussian Naive Bayes (GNB), and Support Vector Machine (SVM), and Decision Tree (DT) in predicting total cases and total deaths of Covid-19 in Indonesia. First, a preprocessing is applied to change the string data to the numerical dataset using a label encoder. Second, the models are trained using the Covid-19 Indonesia Time Series All Dataset (CITSAD) with 90% and 10% train/test split. The three models are then investigated to predict new cases and new deaths. The evaluation using the CITSAD of ten provinces in Indonesia shows that DT gives the highest accuracy of 93% and provides the fastest processing time of 48.4 seconds.

Subjek

Machine Learning
 

Katalog

Prediction of Covid-19 Infection in Indonesia Using Machine Learning Methods
 
 
Indonesia

Sirkulasi

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Pengarang

HASNA ZAKIYYAH
Perorangan
Suyanto
 

Penerbit

Universitas Telkom, S1 Informatika (International Class)
Bandung
2021

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