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

Jurnal Teknologi dan Sistem Komputer

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Title 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
 
Creator Sumari, Arwin Datumaya Wahyudi
Putra, Dimas Rossiawan Hendra
Musthofa, Muhammad Bisri
Mari, Ngat
 
Subject knowledge growing system; moving average; pandemi; pearson correlation coefficient; prediksi
knowledge growing system; moving average; pandemic; prediction; Covid-19
 
Description Penelitian ini bertujuan untuk menganalisis perbandingan kinerja metode-metode komputasi untuk memprediksi dinamika pandemi di Pulau Jawa berdasarkan data-data antara bulan Maret-Mei 2020 yang mencakup Provinsi DKI Jakarta, Jawa Barat, Jawa Tengah, DI Yogyakarta, dan Jawa Timur. Prediksi dilakukan menggunakan tiga metode, yaitu Knowledge Growing System (KGS) dan model deret waktu, yaitu Single Moving Average (SMA), dan Exponential Moving Average (EMA). Berdasarkan dari hasil-hasil komputasi Mean Absolute Percentage Error (MAPE) disimpulkan bahwa metode EMA menghasilkan tingkat kesalahan yang lebih kecil daripada metode SMA dengan rerata sebesar 47,94 %. KGS menghasilkan kompurasi Degree of Certainty (DoC) dan menganalisis tren dinamika pandemi di Provinsi DKI Jakarta akan turun, jika kebijakan yang saat ini diterapkan tetap dilanjutkan. Pada provinsi-provinsi lainnya, KGS memprediksi bahwa dinamika pandemi masih akan terus meningkat.
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.
 
Publisher Departemen Teknik Komputer, Fakultas Teknik, Universitas Diponegoro
 
Date 2021-01-31
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/13779
10.14710/jtsiskom.2020.13779
 
Source Jurnal Teknologi dan Sistem Komputer; Volume 9, Issue 1, Year 2021 (January 2021); 31-40
Jurnal Teknologi dan Sistem Komputer; Volume 9, Issue 1, Year 2021 (January 2021); 31-40
2338-0403
 
Language ind
 
Relation https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/view/13779/12658
https://jtsiskom.undip.ac.id/index.php/jtsiskom/article/downloadSuppFile/13779/501
 
Rights Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
https://creativecommons.org/licenses/by-sa/4.0
 

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