MODEL LOGISTIK FUZZY DENGAN ADANYA PEMANENAN PROPORSIONAL
Abstract
The logistic growth model with proportional harvesting is a population growth model that takes into account harvesting factors. In real life, not all conditions can be known with certainty, such as different growth rates in each population and harvest rates depending on the needs of the harvester. To overcome these conditions, there is a concept that accommodates the problem of uncertainty, namely the fuzzy concept. This concept can be induced into a logistic model with proportional harvesting which assumes the intrinsic growth rate and the harvest rate is expressed by fuzzy numbers. The purpose of this research is to form a logistic model with fuzzy proportional harvesting, analyze the stability of the model, and form a numerical simulation. This study uses the alpha-cut method to generalize the intrinsic growth rate and harvest rate from crisp numbers to fuzzy numbers, then the Graded Mean Integration Representation (GMIR) method to defuzzify the model, and the linearization method to analyze the stability of the model. The results of this study obtained a logistic model with proportional harvesting. Then the model was developed into a logistic model with fuzzy proportional harvesting by assuming the intrinsic growth rate and the harvest rate expressed by fuzzy numbers. From the model obtained 2 equilibrium points, namely the first equilibrium point is unstable and the second equilibrium point is asymptotically stable under certain conditions. Model simulation is given to show illustration of stability analysis. From the simulation, it can also be shown that the higher the graded mean value, the lower the population.
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Ahmad, M. Z., & Hasan, M. K. (2012). Modeling of Biological Populations Using Fuzzy Differential Equations. International Journal of Modern Physics: Conference Series, 09, 354–363. https://doi.org/10.1142/s2010194512005429
Cain, J. W., & Reynolds, A. M. (2010). Ordinary and Partial Differential Equations: An Introduction to Dynamical Systems. Virginia.
Chen, S. H., & Hsieh, C. H. (1997). Representation, Ranking, Distance, and Similarity of L-R type fuzzy number and Application. Australian Journal of Intelligent Processing Systems, 6(4), 217–229.
Chen, S. H., Wang, S. T., & Chang, S. M. (2006). Some properties of graded mean integration representation of L-R type fuzzy numbers. Tamsui Oxford Journal of Information and Mathematical Sciences, 22(2), 185–208.
Doust, R. M. H., & Saraj, M. (2015). The logistic modeling population; having harvesting factor. Yugoslav Journal of Operations Research, 25(1), 107–115. https://doi.org/10.2298/YJOR130515038R
Hale, J. K., & Kocak, H. (1991). Dynamics and Bifurcations. Springer-Verlag New York, United States of America.
Hidayati, T. (2018). Kestabilan Model Populasi Mangsa Pemangsa Dengan Laju Pemanenan Tetap. Delta: Jurnal Ilmiah Pendidikan Matematika, 6(1), 38–46.
Martcheva, M. (2015). An Introduction to Mathematical Epidemiology. Springer, New York.
Ndii, M. Z. (2018). Pemodelan matematika Dinamika Populasi dan Penyebaran Penyakit. Deepublish, Sleman.
Nurrobi, F., Yulida, Y., & Faisal. (2017). Bifurkasi Pada Model Logistik dengan Faktor Pemanenan Konstan. Seminar Nasional Matematika Dan Terapannya I Program Studi Matematika FMIPA ULM Banjarbaru, 36–41.
Otto, S., & Day, T. (2007). A Biologist’s Guide to Mathematical Modeling in Ecology and Evolution. Princeton University Press, New Jersey.
Pal, D., Mahaptra, G. S., & Samanta, G. P. (2012). A Proportional Harvesting Dynamical Model with Fuzzy Intrinsic Growth Rate and Harvesting Quantity. Pac Asian J Math, 6(2), 199–213.
Paul, S., Mondal, S. P., & Bhattacharya, P. (2016). Discussion on fuzzy quota harvesting model in fuzzy environment: fuzzy differential equation approach.
Modeling Earth Systems and Environment, 2(2), 1–15. https://doi.org/10.1007/s40808-016-0113-y
Rahmi, E., & Panigoro, H. S. (2017). Pengaruh Pemanenan terhadap Model Verhulst dengan Efek Allee. SEMIRATA MIPAnet Vol. 1, 105–112.
Susilo, F. (2006). Himpunan & Logika Kabur Serta Aplikasinya. Graha Ilmu, Yogyakarta.
Yulida, Y. (2019). Persamaan Diferensial Biasa. CV. IRDH, Malang.
Yulida, Y., & Karim, M. A. (2021). Prediction of rice consumption in South Kalimantan by considering population growth rate. IOP Conference Series: Earth and Environmental Science, 758(1). https://doi.org/10.1088/1755-1315/758/1/012022
Zadeh, L. A. (1965). Fuzzy Sets*. Information and Control, 8, 338–353.
DOI: https://doi.org/10.20527/epsilon.v16i1.5552
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