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Rifki Ilham Baihaki
Universitas Jember
Computer Science & IT Other

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Perbandingan Metode Extended Kalman Filter dan Ensamble Kalman Filter dalam Mengestimasi Pertumbuhan Sel Kanker dengan Pengobatan Virus Oncolytic Rifki Ilham Baihaki; Didik Khusnul Arif; Erna Apriliani
CGANT JOURNAL OF MATHEMATICS AND APPLICATIONS Vol 4, No 1 (2023): CGANT JOURNAL OF MATHEMATICS AND APPLICATIONS
Publisher : jcgant

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Abstract

Virus is a microorganism that can spread and infect living cells, such as humans, animals, and plants. Not all viruses have negative effects, as in the case of oncolytic viruses. This type of virus is modified to infect and kill cancer cells. The success of cancer therapy using this virus depends on the pattern of interaction between the virus population and cancer cells, which can be described by a mathematical model. This research uses two methods to estimate the growth of cancer cells with oncolytic virus therapy, namely the Extended Kalman Filter (EKF) and the Ensemble Kalman Filter (EnKF). The results show that EKF has a faster computation time compared to EnKF. However, the EKF estimation results are still inferior to those of EnKF.
Perbandingan Metode Extended Kalman Filter dan Ensamble Kalman Filter dalam Mengestimasi Pertumbuhan Sel Kanker dengan Pengobatan Virus Oncolytic Rifki Ilham Baihaki; Didik Khusnul Arif; Erna Apriliani
CGANT JOURNAL OF MATHEMATICS AND APPLICATIONS Vol 4, No 1 (2023): CGANT JOURNAL OF MATHEMATICS AND APPLICATIONS
Publisher : jcgant

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25037/cgantjma.v4i1.93

Abstract

Virus is a microorganism that can spread and infect living cells, suchas humans, animals, and plants. Not all viruses have negative effects, as in thecase of oncolytic viruses. This type of virus is modified to infect and kill cancercells. The success of cancer therapy using this virus depends on the pattern ofinteraction between the virus population and cancer cells, which can bedescribed by a mathematical model. This research uses two methods to estimatethe growth of cancer cells with oncolytic virus therapy, namely the ExtendedKalman Filter (EKF) and the Ensamble Kalman Filter (EnKF). The results showthat EKF has a faster computation time compared to EnKF. However, the EKFestimation results are still inferior to those of EnKF.
Co-Authors Didik Khusnul Arif Erna Apriliani