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
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@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} }
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