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

Pandemic dynamics prediction in Java using the Moving Average and the Knowledge Growing System methods

1Master Program in Electrical Engineering, Politeknik Negeri Malang. Jl. Soekarno Hatta No.9, Malang, Jawa Timur 65141, Indonesia

2Faculty of Defense Technology, Universitas Pertahanan. Integrated Peace and Security Center, Sentul, Bogor, Jawa Barat 10430, Indonesia

Received: 10 Jun 2020; Revised: 18 Oct 2020; Accepted: 24 Oct 2020; Published: 31 Jan 2021; Available online: 27 Oct 2020.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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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