RAFA, HAFIZH SHAFWAN and Rini, Dian Palupi and Marieska, Mastura Diana (2022) OPTIMASI FUZZY TIME SERIES CHEN PADA PREDIKSI KASUS COVID-19 DI SUMATERA SELATAN MENGGUNAKAN PARTICLE SWARM OPTIMIZATION. Undergraduate thesis, Sriwijaya University.
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Abstract
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
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | COVID-19, Particle Swarm Optimization, Fuzzy Time Series Chen |
| Subjects: | P Language and Literature > PM Hyperborean, Indian, and Artificial languages > PM8001-8995 Artificial languages--Universal languages Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. Q Science > QA Mathematics > QA299.6-433 Analysis > Q334.A755 Artificial intelligence. Computational linguistics. Computer science. |
| Divisions: | 09-Faculty of Computer Science > 55201-Informatics (S1) |
| Depositing User: | mr HAFIZH SHAFWAN RAFA |
| Date Deposited: | 18 Jul 2022 02:21 |
| Last Modified: | 18 Jul 2022 02:21 |
| URI: | http://repository.unsri.ac.id/id/eprint/74047 |
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