Main Article Content
Price is one of the important things that need to concern as defining factor of the profit or loss of product selling as the result of price fluctuations that are very difficult to control. Price fluctuations are caused by many factors including weather, stock availability, demand and others. One of the steps to solve the price fluctuations problem is by making a forecast of fish incoming prices. The purpose of this study is to apply Markov chain’s fuzzy time series to forecast farming fish prices. Markov chain fuzzy time series is one of the prediction methods to predict time series data that has advantages in the implentation of historical data, flexible, and high level of data forecasting accuracy. This study used fish prices at November 2018. The results showed that markov chain fuzzy time series showed very accurate forecasting results with a mean error percentage of absolute percentage error (MAPE) of 1.4% so the accuracy of the Markov chain fuzzy time series method is 98, 6%.
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
 Boaisha, S.M., dan Amaitik, S.M Forecasting Model Based on Fuzzy Time Series Approach. Proceedings of the 10th International Arab Conference on Information Technology-ACIT. . 2010.
 Chen, T.L., Cheng, H.C., dan Teoh, H.J., Fuzzy time series based on sequence fo stock price forecasting. International Journal of Physica Vol. 380, (2007) 3777-390., 2007
 Cheng, C.H., Cheng, G.W., dan Wang, J.W., Multi-attribut fuzzy time series method based on fuzzy clistering, International Journal of Expert System with Application Vol. 34 (2008) 1235-1242., 2008
Halim, Siana dan Chandra, Arief.. Pemodelan Time Series Multivariat secara Automatis. Jurnal Teknik Industri Vol 13. 2011
 Hasan,M Iqbal. Pokok-Pokok Materi Statistik 1/Statistik Deskriptif. Jakarta: Bumi Aksara.2002
 Haryono, A., Agus, W., dan Sobri, A. Kajian Model Automatic Clustering- FTS-Markov Chain dalam Memprediksi Data Historis Jumlah Kecelakaan Lalu Lintas Di Kota Malang. Jurnal Sains Dasar. 2013
 Hyndman, R.J., 2014, Forecasting : Principles & Practice, Edition 23th September 2014, University of Western Australia.
 KKP. Potensi Ekonomi Kelautan dan Perikanan. www.kkp.go.id. 2012
Naim, Iram., Tripti, M., dan Ashraf, R.I, Effective Short-Term Forecasting for Daily Time Series with Complex Seasonal Patterns, International Conference on Computational Intelligence and Data Science, Procedia Computer Science 132, 1832-1841. 2018
Qiu, W., dan Liu, X., Li, H., A generalized method for forecasting based on fuzzy time series, International Journal of Expert System with Application Vol.38, 10446-10453. , 2011
Ross, S.M. Introduction to probability models. Academic press. California.,2007
Shalahuddin. Rekayasa Perangkat Lunak Testur dan Berorientasi Objek (Vol.Cetakan Kedua). Bandung : Informatika. .2014
Singh, S,R., A computational method of forecasting based on high –order fuzzy time series, International Journal of Expert System with Application Vol. 36 (2009) 10551-10559 , 2009
Sommerville, Lan. Software Engineering (9th.ed.). Boston : Addison -Wesley. 2011
Song, Q., dan Chissom, B.S., Forecasting enrollments with fuzzy time series, International Journal of Fuzzy Set and System Vol 54 (1993)1-9. , 1993
Tsaur, R.C., dan Kuo, T.C., The Adaptive fuzzy time sries model with an application to Taiwan’s tourism demand, Internaional Journal of Expert System with Applications, Vol. 38,9164-9171. , 2011
Tsaur, R.C., A fuzzy time series-Markov chain model with an application to forecast the exchange rate between the Taiwan and US dollar, International Journal of innovative Computing, Information and Control, Vol 8, 4931-4942 . ,2012
Wei, W.W.S. Time Series Analysis Univariate and Multivariate Methods, Addison-Wesley Company Inc., New York. . 2006