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
BibTex Citation Data :
@article{JTSISKOM13779, author = {Arwin Datumaya Wahyudi Sumari and Dimas Rossiawan Hendra Putra and Muhammad Bisri Musthofa and Ngat Mari}, title = {Prediksi dinamika pandemi di Pulau Jawa menggunakan metode Moving Average dan Knowledge Growing System}, journal = {Jurnal Teknologi dan Sistem Komputer}, volume = {9}, number = {1}, year = {2021}, keywords = {knowledge growing system; moving average; pandemic; prediction; Covid-19}, abstract = {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.}, issn = {2338-0403}, pages = {31--40} doi = {10.14710/jtsiskom.2020.13779}, url = {https://jtsiskom.undip.ac.id/article/view/13779} }
Refworks Citation Data :
Note: This article has supplementary file(s).
Article Metrics:
Last update:
A perspective on a non-binary knowledge growing system in a pattern recognition use-case
A Fast Electrical Distribution Fault Predictor using Knowledge Growing System (KGS)
A Simple Introduction to Cognitive Artificial Intelligence’s Knowledge Growing System
Last update: 2024-11-12 12:13:34
Starting from 2021, the author(s) whose article is published in the JTSiskom journal attain the copyright for their article and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. By submitting the manuscript to JTSiskom, the author(s) agree with this policy. No special document approval is required.
The author(s) guarantee that:
The author(s) retain all rights to the published work, such as (but not limited to) the following rights:
Suppose the article was prepared jointly by more than one author. Each author submitting the manuscript warrants that all co-authors have given their permission to agree to copyright and license notices (agreements) on their behalf and notify co-authors of the terms of this policy. JTSiskom will not be held responsible for anything arising because of the writer's internal dispute. JTSiskom will only communicate with correspondence authors.
Authors should also understand that their articles (and any additional files, including data sets and analysis/computation data) will become publicly available once published. The license of published articles (and additional data) will be governed by a Creative Commons Attribution-ShareAlike 4.0 International License. JTSiskom allows users to copy, distribute, display and perform work under license. Users need to attribute the author(s) and JTSiskom to distribute works in journals and other publication media. Unless otherwise stated, the author(s) is a public entity as soon as the article is published.