OPTIMASI NILAI RENTANG FUZZY TIME SERIES CHEN HSU MENGGUNAKAN PARTICLE SWARM OPTIMIZATION

CHRISTIAN, MARCELL and Dian Palupi, Rini (2024) OPTIMASI NILAI RENTANG FUZZY TIME SERIES CHEN HSU MENGGUNAKAN PARTICLE SWARM OPTIMIZATION. Undergraduate thesis, Sriwijaya University.

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Abstract

Investment is an activity that is full of risk due to price volatility, even though the things invested in are investment goods that tend to be said to be safe, such as gold, crude oil, and shares of large companies such as PT. BCA, to ensure that it does not experience losses, it is best to create a prediction or forecasting system. A prediction system using Fuzzy Time Series is useful for using previous price data to predict prices for the coming day. In the Fuzzy Time Series, there are various models, including the Chen-Hsu model, but this model has the disadvantage of determining interval ranges that are less precise. For this reason, an optimization algorithm is needed for this interval so that forecasting results can be better. For this reason, this research uses Particle Swarm Optimization as an optimization algorithm, the Chen-Hsu FTS model as a forecasting method and Mean Absolute Percentage Error (MAPE) as a benchmark for error. The results of this optimization test produce error values for forecasting oil, gold, and the share price for PT.BCA is 2.3287%, 0.7375%, and 1.0940%, respectively, smaller than the unoptimized forecasting of 2.6%, 0.85%, and 1.64% respectively.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Fuzzy Time Series, Particle Swarm Optimization
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics > Q325.5 Machine learning 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:	Christian Marcell
Date Deposited:	20 Aug 2024 01:39
Last Modified:	20 Aug 2024 01:39
URI:	http://repository.unsri.ac.id/id/eprint/155533

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