Details

Autor(en) / Beteiligte

• Yulita, I N
• Abdullah, A S
• Helen, A
• Hadi, S
• Sholahuddin, A
• Rejito, J

Titel

Comparison multi-layer perceptron and linear regression for time series prediction of novel coronavirus covid-19 data in West Java

Ist Teil von

• Journal of physics. Conference series, 2021-01, Vol.1722 (1), p.12021

Ort / Verlag

Bristol: IOP Publishing

Erscheinungsjahr

2021

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• Until now, the pandemic conditions of Covid-19 are still ravaging the world, even in Indonesia and West Java. Various attempts have been made to stop it. West Java implements Large Scale Social Restrictions, is known as Pembatasan Sosial Skala Besar (PSBB). However, over time, a discourse emerged to loosen PSBB. One of the World Health Organization's (WHO) requirements to loosen is the effective reproduction rate of Corona Virus cases below 1. Therefore, this study focuses on predicting the number of cases in West Java. The methods based on multi-layer perception (MLP) and linear regression (LR). The data were obtained from the C Covid -19 positive case from March to mid-August 2020 in West Java. The experiments show that MLP reaches optimal if it used 13 hidden layers with learning rate and momentum = 0.1. The MLP had a smaller error than LR. Both of them predict the number of cases in the next 30 days from August 14, 2020. The results show that West Java will still have an increase in the number of new cases of Covid -19.