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OPTIMASI FUZZY TIME SERIES CHEN PADA PREDIKSI KASUS COVID-19 DI SUMATERA SELATAN MENGGUNAKAN PARTICLE SWARM OPTIMIZATION

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Penilaian

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become worried about the possibility that would happen in the future. Prediction of COVID-19 cases is a solution that can be done to reduce this worry. This study uses the Fuzzy Time Series Chen method to predict COVID-19 cases in the future, but on the other hand this method has shortcomings in determining the length of the interval which can result in the prediction accuracy being less good, so a Particle Swarm Optimization algorithm is needed to optimize the length. intervals that will later be used to predict cases of COVID-19, so that the results of the predictions will be better. Prediction accuracy is calculated using Mean Absolute Percentage Error. Based on testing the MAPE error value generated from Fuzzy Time Series Chen which is optimized for 26.380%, while for predictions without optimization it produces a value of 30.057%. Keywords: COVID-19, Particle

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2207002103	T74047	T740472022	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T740472022

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

viii, 81116 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.730 7

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Jurusan Teknik Informatika
Struktur Data

Specific Detail Info

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Statement of Responsibility

SEPTA

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