APRISA, TIARA and Rini, Dian Palupi (2024) OPTIMASI FUZZY TIME SERIES MENGGUNAKAN ALGORITMA GENETIKA UNTUK PERAMALAN HARGA BRENT OIL. Undergraduate thesis, Sriwijaya University.
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Abstract
Brent oil is one of the major types of crude oil that serves as a benchmark for global oil prices, particularly in Europe, Africa, and the Middle East. The fluctuation in oil prices has a significant impact on production costs and global economic stability, making oil price forecasting crucial. In this study, Fuzzy Time Series is used to identify historical patterns and predict future prices. Cheng's Fuzzy Time Series differs from other fuzzy methods due to the presence of weight matrices and adaptive forecasting. However, the Fuzzy Time Series algorithm has a drawback in that the interval value sets are too wide, leading to less optimal forecasts. Therefore, the Genetic Algorithm is applied to optimize the intervals in the Fuzzy Time Series, thus improving forecasting accuracy. The results show that the optimized model achieved a Mean Absolute Percentage Error (MAPE) of 2.7931%, which is 38% lower than the Cheng Fuzzy Time Series algorithm without optimization, which had a MAPE of 4.52%. The implementation of this model is expected to make a significant contribution to decision-making in the oil and gas industry as well as other related sectors.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Brent oil, Fuzzy Time Series, Cheng Fuzzy Time Series, Genetic Algorithm |
| Subjects: | Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA75-76.95 Calculating machines > QA76 Computer software T Technology > T Technology (General) > T1-995 Technology (General) T Technology > T Technology (General) > T57.6-57.97 Operations research. Systems analysis > T57.84 Heuristic programming |
| Divisions: | 09-Faculty of Computer Science > 55201-Informatics (S1) |
| Depositing User: | Tiara Aprisa |
| Date Deposited: | 21 Aug 2024 04:55 |
| Last Modified: | 21 Aug 2024 04:55 |
| URI: | http://repository.unsri.ac.id/id/eprint/155726 |
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