How to cite this paper
Salsabila, A., Firdaniza, F., Ruchjana, B & Abdullah, A. (2023). Python script fuzzy time series Markov chain model for forecasting the number of diseases cocoa plant in Bendungan district.International Journal of Data and Network Science, 7(2), 627-636.
Refrences
Alyousifi, Y., Othman, M., Husin, A., & Rathnayake, U. (2021). A new hybrid fuzzy time series model with an application to predict PM10 concentration. Ecotoxicology and Environmental Safety, 227. https://doi.org/10.1016/j.ecoenv.2021.112875
BBPPTP. (2023). https://simopt-bbpptp-surabaya.net. (Online: Accessed January 1, 2023)
Canlas, F. Q., Nair, S., & Paat, I. D. (2022). Exploring COVID-19 Vaccine Side Effects: A Correlational Study Using Py-thon. Procedia Computer Science, 201(C), 752–757. https://doi.org/10.1016/j.procs.2022.03.102
Cascio, W. F., & Montealegre, R. (2016). How Technology Is Changing Work and Organizations. Annual Review of Or-ganizational Psychology and Organizational Behavior, 3, pp. 349–375. https://doi.org/10.1146/annurev-orgpsych-041015-062352
Chen, S.M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81.
Hashim, H. (2018). Application of Technology in the Digital Era Education. International Journal of Research in Coun-seling and Education, 1(2), 1. https://doi.org/10.24036/002za0002
Kanewala, U., & Bieman, J. M. (2014). Testing scientific software: A systematic literature review. Information and Soft-ware Technology, 56, 10, 1219–1232. https://doi.org/10.1016/j.infsof.2014.05.006
Kemenperin. (2023). https://www.kemenperin.go.id. (Online: Accessed January 1, 2023)
Langtangen, H. P. (2008). Python Scripting for Computational Science, 3rd ed. Berlin Heidelberg: Springer-Verlag.
Lawrence, K. D., Klimberg, R. K., & Lawrence, S. M. (2009). Fundamentals of Forecasting Using Excel. Industrial Press Inc.
Leon-Alcaide, P., Rodriguez-Benitez, L., Castillo-Herrera, E., Moreno-Garcia, J., & Jimenez-Linares, L. (2020). An evolu-tionary approach for efficient prototyping of large time series datasets. Information Sciences, 511, 74–93. https://doi.org/10.1016/j.ins.2019.09.044
Listewnik, K. J., & Aftewicz, K. (2023). Rotary 3D Magnetic Field Scanner for the Research and Minimization of the Magnetic Field of UUV. Sensors, 23(1). https://doi.org/10.3390/s23010345
Lutz, M. (2013). Learning Python. Sebastopol: O’REILLY.
Paper, D. (2021). TensorFlow 2.x in the Colaboratory Cloud : An Introduction to Deep Learning on Google’s Cloud Ser-vice. Apress. https://doi.org/https://doi.org/10.1007/978-1-4842-6649-6 ISBN-13
Petrelli, M. (2021). Introduction to Python in Earth Science Data Analysis : From Descriptive Statistics to Machine Learning. Springer: Springer Textbooks in Earth Sciences, Geography and Environment. https://doi.org/https://doi.org/10.1007/978-3-030-78055-5
Sayama, H., Pestov, I., Schmidt, J., Bush, B. J., Wong, C., Yamanoi, J., & Gross, T. (2013). Modeling complex systems with adaptive networks. Computers and Mathematics with Applications, 65(10), 1645–1664. https://doi.org/10.1016/j.camwa.2012.12.005
Song, Q., & Chissom, B. S. (1993). Forecasting enrollments with fuzzy time series - Part I. Fuzzy Sets and Systems, 54(1), 1–9. https://doi.org/10.1016/0165-0114(93)90355-L
Song, Q., & Chissorn, B. S. (1994). Forecasting enrollments with fuzzy time series-part II. In Fuzzy Sets and Systems, 62.
Tsaur, R. C. (2012). A fuzzy time series-Markov chain model with an application to forecast the exchange rate between the Taiwan and us Dollar. International Journal of Innovative Computing, Information and Control, 8(7 B), 4931–4942.
Wang, Y., Chen, Z., Tian, S., Zhou, S., Wang, X., Xue, L., & Wu, J. (2022). Convolutional Neural Network-Based ECG-Assisted Diagnosis for Coal Workers. Environmental Research and Public Health, 20, 17. https://doi.org/10.3390/ijerph
Zadeh, L. A. (1996). Fuzzy Sets. Information and Control, 394–432. https://doi.org/10.1142/9789814261302_0021
Zhu, T., & Liu, G. (2023). A Novel Hybrid Methodology to Study the Risk Management of Prefabricated Building Supply Chains: An Outlook for Sustainability. Sustainability, 15(1). https://doi.org/10.3390/su15010361
Zwicke, F., Knechtges, P., Behr, M., & Elgeti, S. (2016). Automatic implementation of material laws: Jacobian calculation in a finite element code with TAPENADE. Computers and Mathematics with Applications, 72(11), 2808–2822. https://doi.org/10.1016/j.camwa.2016.10.010
BBPPTP. (2023). https://simopt-bbpptp-surabaya.net. (Online: Accessed January 1, 2023)
Canlas, F. Q., Nair, S., & Paat, I. D. (2022). Exploring COVID-19 Vaccine Side Effects: A Correlational Study Using Py-thon. Procedia Computer Science, 201(C), 752–757. https://doi.org/10.1016/j.procs.2022.03.102
Cascio, W. F., & Montealegre, R. (2016). How Technology Is Changing Work and Organizations. Annual Review of Or-ganizational Psychology and Organizational Behavior, 3, pp. 349–375. https://doi.org/10.1146/annurev-orgpsych-041015-062352
Chen, S.M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81.
Hashim, H. (2018). Application of Technology in the Digital Era Education. International Journal of Research in Coun-seling and Education, 1(2), 1. https://doi.org/10.24036/002za0002
Kanewala, U., & Bieman, J. M. (2014). Testing scientific software: A systematic literature review. Information and Soft-ware Technology, 56, 10, 1219–1232. https://doi.org/10.1016/j.infsof.2014.05.006
Kemenperin. (2023). https://www.kemenperin.go.id. (Online: Accessed January 1, 2023)
Langtangen, H. P. (2008). Python Scripting for Computational Science, 3rd ed. Berlin Heidelberg: Springer-Verlag.
Lawrence, K. D., Klimberg, R. K., & Lawrence, S. M. (2009). Fundamentals of Forecasting Using Excel. Industrial Press Inc.
Leon-Alcaide, P., Rodriguez-Benitez, L., Castillo-Herrera, E., Moreno-Garcia, J., & Jimenez-Linares, L. (2020). An evolu-tionary approach for efficient prototyping of large time series datasets. Information Sciences, 511, 74–93. https://doi.org/10.1016/j.ins.2019.09.044
Listewnik, K. J., & Aftewicz, K. (2023). Rotary 3D Magnetic Field Scanner for the Research and Minimization of the Magnetic Field of UUV. Sensors, 23(1). https://doi.org/10.3390/s23010345
Lutz, M. (2013). Learning Python. Sebastopol: O’REILLY.
Paper, D. (2021). TensorFlow 2.x in the Colaboratory Cloud : An Introduction to Deep Learning on Google’s Cloud Ser-vice. Apress. https://doi.org/https://doi.org/10.1007/978-1-4842-6649-6 ISBN-13
Petrelli, M. (2021). Introduction to Python in Earth Science Data Analysis : From Descriptive Statistics to Machine Learning. Springer: Springer Textbooks in Earth Sciences, Geography and Environment. https://doi.org/https://doi.org/10.1007/978-3-030-78055-5
Sayama, H., Pestov, I., Schmidt, J., Bush, B. J., Wong, C., Yamanoi, J., & Gross, T. (2013). Modeling complex systems with adaptive networks. Computers and Mathematics with Applications, 65(10), 1645–1664. https://doi.org/10.1016/j.camwa.2012.12.005
Song, Q., & Chissom, B. S. (1993). Forecasting enrollments with fuzzy time series - Part I. Fuzzy Sets and Systems, 54(1), 1–9. https://doi.org/10.1016/0165-0114(93)90355-L
Song, Q., & Chissorn, B. S. (1994). Forecasting enrollments with fuzzy time series-part II. In Fuzzy Sets and Systems, 62.
Tsaur, R. C. (2012). A fuzzy time series-Markov chain model with an application to forecast the exchange rate between the Taiwan and us Dollar. International Journal of Innovative Computing, Information and Control, 8(7 B), 4931–4942.
Wang, Y., Chen, Z., Tian, S., Zhou, S., Wang, X., Xue, L., & Wu, J. (2022). Convolutional Neural Network-Based ECG-Assisted Diagnosis for Coal Workers. Environmental Research and Public Health, 20, 17. https://doi.org/10.3390/ijerph
Zadeh, L. A. (1996). Fuzzy Sets. Information and Control, 394–432. https://doi.org/10.1142/9789814261302_0021
Zhu, T., & Liu, G. (2023). A Novel Hybrid Methodology to Study the Risk Management of Prefabricated Building Supply Chains: An Outlook for Sustainability. Sustainability, 15(1). https://doi.org/10.3390/su15010361
Zwicke, F., Knechtges, P., Behr, M., & Elgeti, S. (2016). Automatic implementation of material laws: Jacobian calculation in a finite element code with TAPENADE. Computers and Mathematics with Applications, 72(11), 2808–2822. https://doi.org/10.1016/j.camwa.2016.10.010