Main Article Content
The increasing need for fish causes problems related to production in the fisheries sector. In fisheries production all information related to (fishing ground) is well known, but on the other hand it is not easy to predict the amount of production due to unclear information. This is also related to the number of ships that make trips, the length (time) of the trip, the type of fishing gear, weather conditions, the quality of human resources, natural environmental factors, and others. The purpose of this study is to apply Grey forecasting model or GM (1,1) to predict fisheries production. Grey forecasting models are used to build forecast models with limited amounts of data with short-term forecasts that will produce accurate forecasts. This study employs the data of captured fish from 2010 to 2018 to analyze calculations using the GM model (1,1). The results showed that the Grey forecasting model or GM (1.1) produced accurate forecasts with an ARPE error value of 9.60% or the accuracy of the forecast model reached 90.39%.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Notice of Jurnal Ilmiah Kursor, Informatics Engineering, University of Trunojoyo Madura follows Creative Commons Attribution 4.0 International License
 Cui, Jie., Si-feng Liu., Bo Zeng., and Nai-ming Xie., A Novel Grey Forecasting Model and its Optimization, Applied Mathematical Modelling 37, 4399-4406, 2013.
 Diebold, F.X., 2017, Forecasting in Economics, Business, Finance and Beyond, Edition 1st August 2017, University of Pennsylvania.
 Ding, Song., Keith W.H., and Yao-guo Dang., Forecasting China's Electricity Consumption using a New Grey Prediction Model, Energy 149, 314-328, 2018.
 Felthoven, R.G., and Catherine J.M.P., Directions for Productivity Measurement in ﬁsheries, Marine Policy 28, 161-169, 2004.
 Hyndman, R.J., 2014, Forecasting : Principles & Practice, Edition 23th September 2014, University of Western Australia.
 Kaplan, Issac C., and Jerry Leonard., From Krill to Convenience Stores: Forecasting The Economic and Ecological Effects of ﬁsheries Management on the US West Coast, Marine Policy 36, 947-954, 2012.
 Kayacan, Erdal., Baris Ulutas., and Okyay Kaynak., Grey System Theory-Based Models in Time Series Prediction, Expert Systems with Applications 37, 1784-1789, 2010.
 Kim, J.Y., Hyeong C.J., Heeyong K., and Sukyung K., Forecasting the Monthly Abundance of Anchovies in The South Sea of Korea Using a Univariate Approach, Fisheries Research 161, 293-302, 2015.
 Li, Der-Chang., Che-Jung C., Chien-Chih C., and Wen-Svhih C., Forecasting Short-term Electricity Consumption using The Adaptive Grey-Based Approach - An Asian case, Omega 40, 767-773, 2012.
 Ou, Shang-Ling., Forecasting Agricultural Output with An Improved Grey Forecasting Model Based on The Genetic Algorithm, Computers and Electronics in Agriculture 85, 33-39, 2012.
 Sifeng, Liu., Jeffrey Forrest., and Yang Yingjie., A Brief Introduction to Grey Systems Theory, IEEE International Conference on Grey System and Intelligent Services, 1-9, 2011.
 Tsai, Chen-Fang., The Application of Grey Theory to Taiwan Pollution Prediction, Proceedings of the World Congress on Engineering, Vol II, London, July 4-6, 2012.
 Wang, S.J., W-L. Wang., C-T. Huang., and S-C. Chen., Improving Inventory Effectiveness in RFID-Enabled Global Supply Chain with Grey Forecasting Model, Journal of Strategic Information Systems 20, 307-322, 2011.
 Wu, Lifeng., Sifeng L., Ligen Y., and Shuli Y., The Effect of Sample Size on The Grey System Model, Applied Mathematical Modelling 37, 6577-6583, 2013.
 Zhou, Jian-Jun., The Application of Grey Forecasting Model Based on Excel Modeling and Solving in Logistics Demand Forecast, IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing, 362-365, 2013.