OPTIMASI FUZZY TIME SERIES CHENG MENGGUNAKAN PARTICLE SWARM OPTIMIZATION UNTUK HARGA BRENT OIL

ALBIMANZURA, R.M. FARHAN RIZKY and Rini, Dian Palupi and Rodiah, Desty (2022) OPTIMASI FUZZY TIME SERIES CHENG MENGGUNAKAN PARTICLE SWARM OPTIMIZATION UNTUK HARGA BRENT OIL. Undergraduate thesis, Sriwijaya University.

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Abstract

The price of Brent oil is the first choice in investor list of commodities and its constant fluctuations lead to instability in the economy. Therefore, it is important to accurately predict oil price trends to increase profits for investors and benefit the wider community. In this study, the Fuzzy Time Series Cheng model will be used to predict the price of Brent oil. This model will be optimized using the Particle Swarm Optimization (PSO) algorithm to get the optimal interval value by maximizing the available parameter functions. The parameter in the optimized PSO is the number of particles. Number of iterations, w (inertia weight), c1 (speed coefficient 1) and c2 (speed coefficient 2). The test results using the Fuzzy Time Series Cheng produced a MAPE of 3.538%, while the Fuzzy Time Series Cheng test which was optimized for PSO obtained the best fitness value with a MAPE value of 2.964% along with the parameter values, namely the value of the number of iterations = 150, the number of particles = 50, the value of inertial weight = 0.3 and the value of c1 = 1.5 and the value of c2 = 1.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Brent Oil, Forecasting, Fuzzy Time Series Cheng, Particle Swarm Optimization
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. Q Science > QA Mathematics > QA299.6-433 Analysis > Q334.A755 Artificial intelligence. Computational linguistics. Computer science.
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	R.M. Farhan Rizky Albimanzura
Date Deposited:	24 Jan 2023 03:24
Last Modified:	24 Jan 2023 03:24
URI:	http://repository.unsri.ac.id/id/eprint/87350

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