Mudharika, Mudharika (2021) Aspek-Aspek Yang Mempengaruhi Tingkat Akurasi Estimasi Ensemble Kalman Filter (EnKF): (Studi Kasus Penyebaran Demam Berdarah Dengue (DBD) Dan Koronavirus 2019 (Covid-19) Di Jawa Timur). Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Ensemble Kalman Filter (EnKF) method is a modification method of Kalman Filter (KF), Ensemble Kalman Filter method itself is an estimate of variable circumstances for non linear dynamic system. In ensemble kalman filter accuracy estimation there are parameters that affect the accuracy result including initial estimation value (X_0), initial estimated error covariance (P), system noise covariance (Q) and measurement noise (R). The selection of values is done by trial error in the parameters P, Q and R where when determining the range of values the author refers to the value of the data used. The system in the Ensemble Kalman Filter estimation is a non linear dynamic system so the case raised by the author is a non-linear system case. In this final task the authors took two case studies so as to get better simulation results of accuracy. The first case study was a virus originating in Wuhan city of China's Hubei province, namely coronavirus 2019 or often called Covid-19. Covid-19 is an infectious disease caused by SARS-CoV-2. The second case study was dengue fever or DBD for short. Diseases derived from dengue virus due to the bite of the Aedes Aegypti mosquito. Data on dengue fever is taken from the Health Office of East Java Province, while Covid-19 data is taken from the official page of covid-19. From several experiments obtained results when P=R=0.01 and Q=1000 while N ensembles were given 10, 100 and 1000. The best error estimation Ensemble Kalman Filter results were dengue fever cases for vulnerable populations infected by 0.0050, infected populations of 0.0034 and cured populations of 0.0042 with computational time of 5.04152 seconds. While the best error estimation Ensemble Kalman Filter results of Covid-19 cases for the confirmed population of 0.2484, the latent population of 9.8547, the infected population of 1.1017, and the cured population of 8.4883 with the computational time is 6.03739 seconds. From the results of both simulations, the influence of P,Q and R parameters for P=R and Q>R was obtained in the case studies of DBD and Covid-19.
| Item Type: | Thesis (Other) |
|---|---|
| Additional Information: | RSMa 519.544 Mud a-1 |
| Uncontrolled Keywords: | Model Demam Berdarah Dengue (DBD), Model Koronavirus 2019 (COVID-19), Ensemble Kalman Filter (EnKF), Dengue Hemorrhagic Fever (DBD) Model, Coronavirus Model 2019 (COVID-19) |
| Subjects: | H Social Sciences > HA Statistics > HA31.7 Estimation Q Science > QA Mathematics > QA402.3 Kalman filtering. |
| Divisions: | Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis |
| Depositing User: | - Davi Wah |
| Date Deposited: | 19 May 2023 03:54 |
| Last Modified: | 19 May 2023 03:57 |
| URI: | http://repository.its.ac.id/id/eprint/97938 |
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