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All Journal Biosfer: Jurnal Pendidikan Biologi Journal of Innovative and Creativity
Indrati, Dika Agustia
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Education Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Optimizing COVID-19 Epidemiological Models: A Particle Swarm Approach to Parameter Estimation Muniroh, Muna Afdi; Sari, Sekar; Indrati, Dika Agustia
Journal of Innovative and Creativity Vol. 5 No. 2 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

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Abstract

The SEIR (Susceptible-Exposed-Infectious-Recovered) mathematical model, represented as a system of nonlinear differential equations, has proven to be a powerful tool to describe the dynamics of the spread of infectious diseases such as COVID-19. The accuracy of the projection and understanding of this model relies heavily on the proper estimation of its parameters, such as transmission rate, incubation rate, natural birth rate, natural death rate, disease mortality rate, and recovery rate. This study focuses on the development and application of a new approach to estimate crucial parameters in the SEIR model by utilizing Particle Swarm Optimization (PSO). PSO is a metaheuristic optimization algorithm inspired by the social behavior of flocks of birds or schools of fish. PSO was chosen for its outstanding ability to find a global minimum in a complex search space, as well as its efficiency in handling nonlinear optimization problems. The advantage of PSO lies in its effective memory capacity, which allows the storage of previous best values, both individually and globally, thus accelerating convergence to the optimal solution. Through a simulation program, this study successfully identified the optimal set of parameters for the SEIR model. These estimated parameters were then carefully evaluated by comparing the model simulation outputs with available COVID-19 epidemiological data, demonstrating the model's ability to accurately replicate pandemic trends. The results of this study are expected to make a significant contribution to modelling and understanding the spread of COVID-19.
Tren Penelitian Augmented Reality dalam Pembelajaran Biologi: Sistematik Literatur Review dari 2020-2024 Indrati, Dika Agustia; Masing, Feliksitas Angel
Biosfer: Jurnal Pendidikan Biologi Vol. 18 No. 2 (2025): Biosfer: Jurnal Pendidikan Biologi
Publisher : Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/biosferjpb.53911

Abstract

This study is a systematic literature review (SLR). The purpose of this study is to determine research trends related to the use of augmented reality (AR) in biology learning from 2020 to 2024. The method used is the PRISMA method. The database used for article searches is Google Scholar. The keywords used in the article search are "augmented reality," "biology education," and "biology learning". From the search results, 300 articles were obtained. Then, these articles were selected according to the inclusion and exclusion criteria, resulting in 57 articles being reviewed. The reviewed article is an article that comes from both national and international journals. The findings reveal that the trend of research related to the use of augmented reality in biology learning has experienced a significant increase. The highest number of articles related to augmented reality were published in 2024. The trend of the most commonly used research methods is the quantitative method. Cells, viruses, the human skeletal system, and the human digestive system are the most commonly used topics in developing augmented reality for biology learning. Based on the dependent variables examined in this SLR study, the most investigated variables are students' cognitive learning outcomes, motivation, and critical thinking skills. In terms of data collection instruments, the most commonly used instrument is the test instrument. Through this SLR, we hope that future studies related to augmented reality will continue to develop considering the rapid advancement of technology. For researchers, they can develop AR research using qualitative or mixed methods to obtain more detailed information or develop other research methods such as CAR or DDR, as they are not yet widely encountered.
Co-Authors Feliksitas Angel Masing Muniroh, Muna Afdi Sari, Sekar