The forecasting of palm oil based on fuzzy time series-two factor

Main Article Content

Ratri Wulandari
Bayu Surarso
Bambang Irawanto
Farikhin Farikhin


Palm oil is a vegetable oil obtained from the mesocarp fruit of the palm tree, generally, from the species, Elaeis guineensis, and slightly from the species Elaeis oleifera and Attalea maripa. Palm oil is naturally red due to its high alpha and beta-carotenoid content. Palm kernel oil is different from palm kernel oil produced from the same fruit core. Planning for palm oil production is necessary because it greatly affects to the level of the country’s economy. Forecasting can reduce uncertainty in planning. Forecasting used in the palm oil problem is two-factor forecasting using the Kumar method with uama factors in the form of palm oil production and supporting factors in the form of land area. The forecasting is evaluated using AFER and MSE, from the acquisition of AFER value of 1.212% <10%, then the forecasting has very good criteria.


Download data is not yet available.

Article Details

How to Cite
R. Wulandari, B. Surarso, B. Irawanto, and F. Farikhin, “The forecasting of palm oil based on fuzzy time series-two factor”, JOSCEX, vol. 2, no. 1, pp. 11-16, Mar. 2021.


I. E. Henson, “A brief history of the oil palm,” Palm Oil Prod. Process. Charact. Uses, pp. 1–29, 2012, doi: 10.1016/B978-0-9818936-9-3.50004-6.

H. Purnomo et al., “Reconciling oil palm economic development and environmental conservation in Indonesia: A value chain dynamic approach,” For. Policy Econ., vol. 111, 2020, doi: 10.1016/j.forpol.2020.102089.

H. Herdiansyah, H. A. Negoro, N. Rusdayanti, and S. Shara, “Palm oil plantation and cultivation: Prosperity and productivity of smallholders,” Open Agric., vol. 5, no. 1, pp. 617–630, 2020, doi: 10.1515/opag-2020-0063.

E. Hambali and M. Rivai, “the potential of palm oil waste biomass in Indonesia in 2020 and 2030,” IOP Conf. Ser. Earth Environ. Sci., vol. 65, no. 1, 2017, doi: 10.1088/1755-1315/65/1/012050.

L. A. Zadeh, “Fuzzy sets,” Inf. Control, vol. 8, no. 3, pp. 338–353, 1965, doi: 10.1016/S0019-9958(65)90241-X.

N. Kurniawan, “Electrical energy monitoring system and Automatic Transfer Switch (ATS) Controller with the internet of things for solar power plants,”. J. Soft Comput. Explor., vol. 1, no. 1. pp. 16–23, 2020.

I. E. Tiffani, “Optimization of naïve bayes classifier by implemented unigram , bigram , trigram for sentiment analysis of hotel review,”. J. Soft Comput. Explor., vol. 1, no. 1, pp. 1–7, 2020.

Q. Song and B. S. Chissom, “Forecasting enrollments with fuzzy time series - Part I,” Fuzzy Sets Syst., vol. 54, no. 1, pp. 1–9, 1993, doi: 10.1016/0165-0114(93)90355-L.

Q. Cai, D. Zhang, W. Zheng, and S. C. H. Leung, “A new fuzzy time series forecasting model combined with ant colony optimization and auto-regression,” Knowledge-Based Syst., vol. 74, pp. 61–68, 2015, doi: 10.1016/j.knosys.2014.11.003.

T. A. Jilani, S. Muhammad, A. Burney, and C. Ardil, “Fuzzy metric approach for fuzzy time series forecasting based on frequency density based partitioning,” Internaltional J. Comput. Electr. Control Inf. Eng., vol. 4, no. 7, pp. 39–44, 2010.

B. Garg, M. M. Sufyan Beg, A. Q. Ansari, and B. M. Imran, “Fuzzy time series prediction model,” Commun. Comput. Inf. Sci., vol. 141 CCIS, pp. 126–137, 2011, doi: 10.1007/978-3-642-19423-8_14.

O. Cagcag Yolcu, U. Yolcu, E. Egrioglu, and C. H. Aladag, “High order fuzzy time series forecasting method based on an intersection operation,” Appl. Math. Model., vol. 40, no. 19–20, pp. 8750–8765, 2016, doi: 10.1016/j.apm.2016.05.012.

R. Wulandari, Farikhin, B. Surarso, and B. Irawanto, “First-order fuzzy time series based on frequency density partitioning for forecasting production of petroleum,” IOP Conf. Ser. Mater. Sci. Eng., vol. 846, no. 1, 2020, doi: 10.1088/1757-899X/846/1/012063.

B. Irawanto, R. W. Ningrum, B. Surarso, and Farikhin, “An improved forecasting method of frequency density partitioning (FDP) based on fuzzy time series (FTS),” J. Phys. Conf. Ser., vol. 1321, no. 2, 2019, doi: 10.1088/1742-6596/1321/2/022082.

M. Y. Chen, “A high-order fuzzy time series forecasting model for internet stock trading,” Futur. Gener. Comput. Syst., vol. 37, pp. 461–467, 2014, doi: 10.1016/j.future.2013.09.025.

S. M. Chen, “Forecasting enrollments based on high-order fuzzy time series,” Cybern. Syst., vol. 33, no. 1, pp. 1–16, 2002, doi: 10.1080/019697202753306479.

R. Zhang, B. Ashuri, and Y. Deng, “A novel method for forecasting time series based on fuzzy logic and visibility graph,” Adv. Data Anal. Classif., vol. 11, no. 4, pp. 759–783, 2017, doi: 10.1007/s11634-017-0300-3.

M. Bose and K. Mali, “Forecasting with multivariate fuzzy time series: A statistical approach,” Adv. Intell. Syst. Comput., vol. 1085, pp. 247–257, 2020, doi: 10.1007/978-981-15-1366-4_20.

F. Li and F. Yu, “Multi-factor one-order cross-association fuzzy logical relationships based forecasting models of time series,” Inf. Sci. (Ny)., vol. 508, pp. 309–328, 2020, doi: 10.1016/j.ins.2019.08.058.

S. Kumar and N. Kumar, “Two factor fuzzy time series model for rice forecasting,” Int. J. Comput. Math. Sci, vol. 4, no. 1, pp. 56–61, 2015.

C. Yasunobu, M. Kosaka, K. Yokomura, and K. Honda, “A decision support system building tool with fuzzy logic and its application to chart technical analysis,” Adapt. Intell. Syst., pp. 19–32, 1992, doi: 10.1016/b978-0-444-89838-8.50005-9.