International Journal of Data Science, Engineering, and Analytics (IJDASEA)
Vol. 1 No. 2 (2021): International Journal of Data Science, Engineering, and Analytics Vol 1, No 2,

Visitor Forecasting Wisata Bahari Lamongan (WBL) Using Hybrid Particle Swarm Optimization (PSO) and Seasonal ARIMA

Dinita Rahmalia (Universitas Islam Darul Ulum Lamongan)

Article Info

Publish Date
25 Nov 2021

Abstract

The revenue of city is determined by some factors, one of them is tourism sector. A problem of tourism sector is forecasting visitors Wisata Bahari Lamongan (WBL). Because data of the number of visitors WBL are fluctuating and seasonal, then it is required Seasonal ARIMA method. In the Seasonal ARIMA method, there are some parameters that should be optimized for producing forecasting with small mean square error (MSE). In this research, Seasonal ARIMA parameters will be optimized by Particle Swarm Optimization (PSO). PSO is optimization algorithm inspired by behavior of birds group in searching food. Based on simulation results, PSO algorithm can optimize Seasonal ARIMA parameter which is optimal and it can produce forecasting result with small MSE.

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Journal Info
International Journal of Data Science, Engineering, and Analytics (IJDASEA)
Website

Abbrev

ijdasea

Publisher

Universitas Pembangunan Nasional Veteran Jawa Timur

Subject

Computer Science & IT Decision Sciences, Operations Research & Management

Description

Focus and Scope The IJDASEA International Journal of Data Science, Engineering, and Analytics publishes original papers in the field of computer science which covers the following scope: 1. Theoretical Foundations: Probabilistic and Statistical Models and Theories Optimization Methods Data ...