Proceedings of the 2023 International Conference on Information Technology and Engineering (ICITE 2023)

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Comparison of Fuzzy Time Series and Fuzzy Time Series-Particle Swarm Optimization Methods in Predicting Bank BCA Share Price

Authors
Prizka Rismawati Arum1, *, Kintoko2, Nur Huriyatullah Rona Nabila3, Eko Andy Purnomo4
1Universitas Muhammadiyah Semarang, Semarang, Indonesia
2Universitas PGRI Yogyakarta, Yogyakarta, Indonesia
3Universitas Muhammadiyah Semarang, Semarang, Indonesia
4Universitas Muhammadiyah Semarang, Semarang, Indonesia
*Corresponding author. Email: kintoko@upy.ac.id
Corresponding Author
Prizka Rismawati Arum
Available Online 23 December 2023.
DOI
10.2991/978-94-6463-338-2_2How to use a DOI?
Keywords
BBCA Stock; Fuzzy Time Series; Particle Swarm Optimization; sMAPE
ABSTRACT

Investment is an investment activity that aims to enable investors or capital owners to benefit from the results of the investment or investment made. One of the profitabel investments is stock investment. PT Bank Central Asia (BBCA) is a private company engaged in banking finance. The Fuzzy Time Series method is a forecasting method that uses artificial intelligence with the ability to capture patterns from past data and then use it to predict future data using fuzzy logic principles. Particle Swarm Optimization is a simple and good optimization algorithm in solving optimization problems. The objective of this research is to compare the Fuzzy Time Series and Fuzzy Time Series-Particle Swarm Optimization methods in predicting the stock price of PT Bank Central Asia (BBCA) from January 3, 2022, to May 9, 2023. Based on the analysis, the sMAPE value obtained for the Fuzzy Time Series method is 1.4701%, while the sMAPE value for the Fuzzy Time Series-Particle Swarm Optimization method is 1.3678%%. From the analysis results, it can be concluded that optimization using Particle Swarm Optimization in the Fuzzy Time Series method produces more optimal prediction values.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 International Conference on Information Technology and Engineering (ICITE 2023)
Series
Advances in Intelligent Systems Research
Publication Date
23 December 2023
ISBN
10.2991/978-94-6463-338-2_2
ISSN
1951-6851
DOI
10.2991/978-94-6463-338-2_2How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Prizka Rismawati Arum
AU  - Kintoko
AU  - Nur Huriyatullah Rona Nabila
AU  - Eko Andy Purnomo
PY  - 2023
DA  - 2023/12/23
TI  - Comparison of Fuzzy Time Series and Fuzzy Time Series-Particle Swarm Optimization Methods in Predicting Bank BCA Share Price
BT  - Proceedings of the 2023 International Conference on Information Technology and Engineering (ICITE 2023)
PB  - Atlantis Press
SP  - 5
EP  - 18
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-338-2_2
DO  - 10.2991/978-94-6463-338-2_2
ID  - Arum2023
ER  -