Application Bat Algorithm for Estimating Super Pairwise Alignment Parameters on Similarity Analysis Between Virus Protein Sequences

Rahmalia, Dinita; Herlambang, Teguh

doi:10.26555/jiteki.v6i2.14323

Cited by 1 publication

(2 citation statements)

References 21 publications

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“…It emphasizes the value placed on making gradual improvements in accuracy, even if it requires investing additional time, as these advancements contribute to a Ì 415 better-informed and empowered financial environment. This is in line with research conducted by Liantoni [25], which shows that forecasting accuracy using exponential smoothing can support decision-making when trading stocks.…”

Section: Discussionsupporting

confidence: 91%

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Enhancing Accuracy in Stock Price Prediction: The Power of Optimization Algorithms

Fitria,

Widayanti

2024

matrik

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The purpose of this research was to improve the accuracy of stock price prediction by implementing optimization algorithms on forecasting methods, in this case, the exponential smoothing method. This research implemented the Particle Swarm Optimization (PSO) and Bat Algorithm metaheuristic optimization algorithms to determine the single-exponential smoothing method’s smoothing parameters. Before implementing the optimization algorithm, the way to determine the smoothing parameters was by trial-and-error method, which is considered less effective. Therefore, the novelty of this research is tuning the parameters of the exponential smoothing method using a comparison of two metaheuristic algorithms, namely the particle swarm optimization algorithm compared to the bat algorithm. The Single Exponential Smoothing method with PSO and Bat algorithms was proven to improve accuracy. The alpha parameter found by the PSO algorithm is 0.9346, and the bat algorithm is 0.936465. With a MAPE of 1.0311%, it was better than the MAPE generated in the Single Exponential smoothing method by trial and error of 1.0316%. This research contributes to providing insight that in a highly sensitive stock prediction situation, metaheuristic algorithms can be used to create more accurate and efficient prediction results.

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Section: Discussionsupporting

confidence: 91%

“…The gradual increase in accuracy leads to more precise forecasts, providing investors and analysts with a competitive advantage to make well-informed choices within the unpredictable realm of finance. This research also supports the finding that PSO [11] and Bat Algorithm [25] can improve stock forecasting results.…”

Section: Discussionsupporting

confidence: 85%

Enhancing Accuracy in Stock Price Prediction: The Power of Optimization Algorithms

Fitria,

Widayanti

2024

matrik

View full text Add to dashboard Cite

show abstract

Application Bat Algorithm for Estimating Super Pairwise Alignment Parameters on Similarity Analysis Between Virus Protein Sequences

Cited by 1 publication

References 21 publications

Enhancing Accuracy in Stock Price Prediction: The Power of Optimization Algorithms

Enhancing Accuracy in Stock Price Prediction: The Power of Optimization Algorithms

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