DOI: https://doi.org/10.14710/jtsiskom.2020.13779

Prediksi dinamika pendemi 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 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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Abstract
This study is aimed 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. Predictions were made using the Knowledge Growing System (KGS) and time series models, such as Single Moving Average (SMA) and Exponential Moving Average (EMA). Based on the computational results of the Mean Absolute Percentage Error (MAPE), the EMA method produces a smaller error rate than the SMA method. The prediction results using KGS with a Degree of Certainty (DoC) produced trend analysis that the dynamics of the pandemic in DKI Jakarta province will decrease if the current policy still implemented and it will continue to decrease step by step. 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; pandemi; pearson correlation coefficient; prediksi
Funding: Politeknik Negeri Malang; Universitas Pertahanan

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