Prediksi dinamika pandemi di Pulau Jawa menggunakan metode Moving Average dan Knowledge Growing System (KGS)

Pandemic dynamics prediction in Java using the Moving Average method and the Knowledge Growing System (KGS)

*Arwin Datumaya Wahyudi Sumari, Dimas Rossiawan Hendra Putra, Muhammad Bisri Musthofa, Ngat Mari | Detail
*Arwin Datumaya Wahyudi Sumari scopus  -  Politeknik Negeri Malang, Indonesia
Dimas Rossiawan Hendra Putra  -  Politeknik Negeri Malang, Indonesia
Muhammad Bisri Musthofa  -  Politeknik Negeri Malang, Indonesia
Ngat Mari  -  Politeknik Negeri Malang, Indonesia
Received: 10 Jun 2020; Revised: 18 Oct 2020; Accepted: 24 Oct 2020; Published: 31 Jan 2021; Available online: 27 Oct 2020.
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Citation Format:
This study aims to analyze the comparative performance of pandemic dynamics prediction methods on the island of Java, based on data from March to May 2020 covering the provinces of DKI Jakarta, West Java, Central Java, DI Yogyakarta, and East Java. The prediction uses Knowledge Growing System (KGS) and time series models, namely Single Moving Average (SMA) and Exponential Moving Average (EMA). Based on the Mean Absolute Percentage Error (MAPE) computational results, the EMA method produces a lower error rate than the SMA method with 47.94 % on average. The KGS prediction with a Degree of Certainty (DoC) produced a trend analysis that the pandemic dynamics in DKI Jakarta province will decrease gradually if the current policy is still implemented. Whereas in the other provinces, the KGS predicted the pandemic dynamics trends will still increase.

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Keywords: knowledge growing system; moving average; pandemic; prediction; Covid-19
Funding: Politeknik Negeri Malang; Universitas Pertahanan

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