Modelling Multi Regression with Particle Swarm Optimization Method to Food Production Forecasting
Keywords:
Forecasting, Multiple Regression, Particle Swarm Optimization, Production?Abstract
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.References
Suwandi, Outlook of Agricultural Commodities Sub Sector of Food Crops. Jakarta: Center for Agricultural Information System and Data of the Ministry of Agriculture, 2016.
Badan Standardisasi Nasional, Tempe : Indonesia’s Offering to the World. 2012.
D. Di and K. Rajeg, “Chemical and Organoleptic Composition of Formula Nugget Based on Tempe Flour and Ricebran Flour,” Indones. J. Hum. Nutr., vol. 1, no. 2, pp. 135–148, 2014.
N. P. Barbosa, E. S. Christo, and K. A. Costa, “Demand forecasting for production planning in a food company,” ARPN J. Eng. Appl. Sci., vol. 10, no. 16, pp. 7137–7141, 2015.
E. C. Daniel, O. U. Patrick, and A. State, “Application of Forecasting Methods for the Estimation of Production Demand By,” vol. 3, no. 2, pp. 184–202, 2014.
F. S. Gharehchopogh, T. H. Bonab, and S. R. Khaze, “A Linear Regression Approach to Prediction of Stock Market Trading Volume: A Case Study,” Int. J. Manag. Value Supply Chain., vol. 4, no. 3, pp. 25–31, 2013.
S. K. Sheikh and M. Hapse, “Unit Costs Estimation in Sugar Plant using Multiple Regression Least Squares Method,” Int. J. of Adv. in Eng. & Tech., vol. 1, no. 4, pp. 299–306, 2011.
С. А. Ярушев, S. A. Yarushev, A. N. Averkin, and A. A.N., “Review of studies on time series forecasting based on hybrid methods, neural networks and multiple regression,” Международный Журнал “Программные Продукты И Системы,” vol. 31, no. 113, pp. 75– 82, 2016.
F. Jiang and W. Wu, “Hybrid Genetic Algorithm and Support Vector Regression Performance in CNY Exchange Rate Prediction,” vol. 2, no. Esm, pp. 136–139, 2016.
N. R. Sari, W. F. Mahmudy, and A. P. Wibawa, “Backpropagation on Neural Network Method for Inflation Rate Forecasting in Indonesia,” Int. J. Adv. Soft Comput. Appl., vol. 8, no. 3, 2016.
R. Koen and J. Holloway, “Application of multiple regression analysis to forecasting South Africa ’ s electricity demand,” J. Energy South. Africa, vol. 25, no. 4, pp. 48–58, 2014.
A. Rahmi and W. F. Mahmudy, “Regression Modelling for Precipitation Prediction Using Genetic Algorithms,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 15, no. 3, 2017.
J. J. Liu, “Time Series Forecasting Using a Hybrid Adaptive Particle Swarm Optimization and Neural Network Model,” vol. 2, no. 4, pp. 335–344, 2014.
Y. Zhang and C. Science, “Prediction of Financial Time Series With,” no. May, 2004.
A. Hayder M. Albehadili Abdurrahman and N. . Islam, “An Algorith for Time Series Prediction Using,” Int. J. Sci. Knowl. Comput. Inf. Technol., vol. 4, no. 6, pp. 26–33, 2014.
S. Han, F. Drive, C. Bldg, and D. W. Halpin, “The Use Of Simulation For Productivity Estimation Based On Multiple Regression Analysis,” 2005, no. 1, pp. 1492–1499.
M. Amin, M. Amanullah, and A. Akbar, “Time Series Modeling for forecasting wheat production of Pakistan,” Plant Sci., vol. 24, no. 5, pp. 1444–1451, 2014.
H.-I. Hsieh, T.-P. Lee, and T.-S. Lee, “A Hybrid Particle Swarm Optimization and Support Vector Regression Model for Financial Time Series Forecasting,” Int. J. Bus. Adm., vol. 2, no. 2, pp. 48–56, 2011.
I. Wahyuni and W. F. Mahmudy, “Rainfall Prediction in Tengger , Indonesia Using Hybrid Tsukamoto FIS and Genetic Algorithm Method,” J. ICT Resourch Appl., vol. 11, no. 1, pp. 38–54, 2017.
D. Novitasari, I. Cholissodin, and W. F. Mahmudy, “Hybridizing PSO With SA for Optimizing SVR Applied to Software Effort Estimation,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 14, no. 1, pp. 245–253, 2016.
D. A. Lind, W. G. Marchal, and S. A. Wathen, Business & Economics Fifth Edition. 2006.
M. Ghiassi, D. Lio, and B. Moon, “Pre-production forecasting of movie revenues with a dynamic artificial neural network,” Expert Syst. Appl., vol. 42, no. 6, pp. 3176–3193, 2015.
R. E. Walpole, R. H. Myers, S. L. Myers, and K. Ye, Probability and Statistics for Engineers and Scientists, vol. 3rd. 2012.
M. Clerc, “Particle swarm optimization,” p. 244, 200AD.
R. C. Eberhart and Y. Shi, “Comparing inertia weights and constriction factors in particle swarm optimization,” IEEE Congr. Evol. Comput., vol. 1, no. 7, pp. 84–88 vol.1, 2000.
A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients,” IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 240–255, 2004.
F. Marini and B. Walczak, “Particle swarm optimization (PSO). A tutorial,” Chemom. Intell. Lab. Syst., vol. 149, pp. 153–165, 2015.
W. F. Mahmudy, R. M. Marian, and L. H. S. Luong, “Optimization of Part Type Selection and Loading Problem with Alternative Production Plans in Flexible Manufacturing System using Hybrid Genetic Algorithms - Part 1 : Modelling and Representation,” in International Conference on Knowledge and Smart Technology (KST), 2013, pp. 75– 80.
M. C. C. Utomo, W. F. Mahmudy, and S. Anam, “Determining the Neuron Weights of Fuzzy Neural Networks Using Multi-Populations Particle Swarm Optimization for Rainfall Forecasting,” J. Telecommun. Electron. Comput. Eng., vol. 9, no. 2, pp. 37–42, 2017.
R. C. Eberhart and Y. Shi, “Comparing inertia weights and constriction factors in particle swarm optimization,” vol. 1, no. 7, pp. 84–88 vol.1, 2000.
Y. P. Anggodo and W. F. Mahmudy, “A Novel Forecasting Based on Autmatic-Optimized Fuzzy Time Series,” TELKOMNIKA (Telecomunication Computing Electronics and Control), vol. 16, no. 4, pp. 1809-1817, 2018.
I. H. Kaya, “Motivation Factors of Consumers ’ Food Choice,” Food Nutr. Sci., vol. 7, no. March, pp. 149–154, 2016.
Downloads
Published
How to Cite
Issue
Section
License
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.
