Oil Demand Forecasting With the Integration of Support Vector Regression (SVR) and Particle Swarm Optimization (PSO)
Peramalan Permintaan Oli Dengan Integrasi Support Vector Regression (SVR) Dan Particle Swarm Optimization (PSO)
DOI:
https://doi.org/10.21070/ups.10354Keywords:
Forecasting, Support Vector Regression, Particle Swarm Optimization, Safety StockAbstract
Inventory control is a problem often faced by companies, such as PT.XYZ, which has not established a safety stock
system for its oil inventory. This is due to fluctuations in oil demand each month, which averages 53%. This study
aims to determine the results of oil forecasting for the next period and produce forecasts with a minimum error rate.
The method used in this study is to integrate machine learning, namely Support Vector Regression (SVR) with Particle
Swarm Optimization (PSO), assisted by the Radial Basis Function (RBF) kernel. This study produced forecast results
with a MAPE value of 18% with optimal parameters C = 199.9993, ε = 0.0744, σ = 0.2487, cLR = 0.1954, and λ =
0.003. The results of this study can then be used by companies as a good reference in determining safe stock and the
optimal number of oil orders.
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