Modelling Multi Regression with Particle Swarm Optimization Method to Food Production Forecasting


  • Adyan Nur Alfiyatin Faculty of Computer Science, Brawijaya University, Malang 65145, Indonesia
  • Wayan Firdaus Mahmudy Faculty of Computer Science, Brawijaya University, Malang 65145, Indonesia
  • Yusuf Priyo Anggodo Data Scientist, Ilmuone Data, Jakarta 12190, Indonesia


Forecasting, Multiple Regression, Particle Swarm Optimization, Production?


Tempe was one of the perishable foods with a durability of 2 to 3 days. Tempe home-based industry must take into account its production in order to avoid losses. Suitable planning and forecasting can determine the ways for the production process is implemented. Previously, regression analysis was used as the method to improve the process. This research proposed the particle swarm optimisation (PSO) and multiple regression for production forecasting. PSO is used for optimisation value of regression variable of the tempe productions while multiple regression is used to determine the best coefficients for forecasting. The result of the research showed that the combination of multiple regression and particle swarm optimisation method performed quite well, indicated by the RMSE value of 2.081641.


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How to Cite

Alfiyatin, A. N., Mahmudy, W. F., & Anggodo, Y. P. (2018). Modelling Multi Regression with Particle Swarm Optimization Method to Food Production Forecasting. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(3), 91–95. Retrieved from

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