* Corresponding Author
Hide
Show all
Peramalan Data IHSG Menggunakan Fuzzy Time Series
https://doi.org/10.22146/ijccs.2155 Seng Hansun(1*)
(1) Universitas Multimedia Nusantara; Jl. Boulevard Gading Serpong, telp.(021)54220808, fax.(021)54220800
(*) Corresponding Author
Abstract
Abstrak
Fuzzy time series merupakan salah satu metode soft computing yang telah digunakan dan diterapkan dalam analisis data runtun waktu. Tujuan utama dari fuzzy time series adalah untuk memprediksi data runtun waktu yang dapat digunakan secara luas pada sembarang data real time, termasuk data pasar modal.
Banyak peneliti yang telah berkontribusi dalam pengembangan analisis data runtun waktu menggunakan fuzzy time series, seperti Chen dan Hsu [1], Jilani dkk. [2], serta Stevenson dan Porter [3]. Dalam penelitian ini, dicoba untuk menerapkan metode fuzzy time series pada salah satu indikator pergerakan harga saham, yakni data IHSG (Indeks Harga Saham Gabungan).
Kinerja metode yang diusulkan dievaluasi dengan menghitung tingkat akurasi dan tingkat kehandalan metode fuzzy time series yang diterapkan pada data IHSG. Melalui pendekatan ini, diharapkan metode fuzzy time series dapat menjadi alternatif untuk memprediksi data IHSG yang merupakan salah satu indikator pergerakan harga saham di Indonesia.
Kata kunci – fuzzy time series, data runtun waktu, soft computing, IHSG
Abstract
Fuzzy time series is one of the soft computing method that been used and implemented in time series analysis. The main goal of fuzzy time series is to predict time series data that can be used widely in any real time data, including stock market share.
Many researchers have contributed in the development of fuzzy time series analysis, such as Chen and Hsu [1], Jilani [2], and Stevenson and Porter [3]. In this research, we will try to implement the fuzzy time series method in one of the stock market change indicator, i.e. the Jakarta composite index or also known as IHSG (Indeks Harga Saham Gabungan).
The research is continued by calculating the accuracy and robustness of the method which has been implemented on IHSG data. By this approach, we hope it can be an alternative to predict the IHSG data which is an indicator of stock price changes in Indonesia.
Keywords – fuzzy time series, time series data, soft computing, IHSG
Keywords
Full Text:
PDFReferences
[1] Chen, S.-M. dan Hsu, C.-C., 2004, A New Method to Forecast Enrollments Using Fuzzy Time Series, International Journal of Applied Science and Engineering, 2, 3, 234-244.
[2] Jilani, T.A., Burney S.M.A., dan Ardil C., 2007, Fuzzy Metric Approach for Fuzzy Time Series Forecasting based on Frequency Density Based Partitioning, World Academy of Science, Engineering and Technology, 34, 1-6.
[3] Stevenson, M. dan Porter, J.E., 2009, Fuzzy Time Series Forecasting Using Percentage Change as the Universe of Discourse, World Academy of Science, Engineering and Technology, 27, 55, 154-157, http://www.waset.org/journals/waset/v55/.
[4] OECD: Glossary of Statistical Terms, http://stats.oecd.org/glossary/about.asp, diakses 20 Maret 2012.
[5] Subanar dan Suhartono, 2009, Wavelet Neural Networks untuk Peramalan Data Time Series Finansial, Program Penelitian Ilmu Dasar Perguruan Tinggi, FMIPA UGM, Yogyakarta.
[6] Boediono dan Koster, W., 2001, Teori dan Aplikasi Statistika dan Probabilitas, PT. Remaja Rosdakarya, Bandung.
[7] Render, B., Stair Jr., R.M. dan Hanna, M.E., 2003, Quantitative Analysis for Management, 8th edition, Pearson Education, Inc., New Jersey.
[8] Popoola, A., Ahmad, S. dan Ahmad, K., 2004, A Fuzzy-Wavelet Method for Analyzing Non-Stationary Time Series, Proc. of the 5th International Conference on Recent Advances in Soft Computing RASC2004, Nottingham, United Kingdom, 231-236.
[9] Popoola, A.O., 2007, Fuzzy-Wavelet Method for Time Series Analysis, Disertasi, Department of Computing, School of Electronics and Physical Sciences, University of Surrey, Surrey.
[10] Hansun, S., 2011, Penerapan Pendekatan Baru Metode Fuzzy-Wavelet dalam Analisis Data Runtun Waktu, Prosiding Seminar Nasional Ilmu Komputer (SEMINASIK) GAMA, Yogyakarta, Indonesia, November 11.
[11] Hansun, S., 2011, Penerapan Pendekatan Baru Metode Fuzzy-Wavelet dalam Analisis Data Runtun Waktu, Tesis, Program Pasca Sarjana Ilmu Komputer, Universitas Gadjah Mada, Yogyakarta.
[12] Wang, L.-X. dan Mendel, J.M., 1992, Generating Fuzzy Rules by Learning from Examples, IEEE Transactions on Systems, Man, and Cybernatics, 22, 6, 1414-1427.
[13] Wang, L.-X., 1996, A Course in Fuzzy Systems and Control, Prentice-Hall International, Inc., United States of America.
DOI: https://doi.org/10.22146/ijccs.2155
Article Metrics
Abstract views : 24201
| 
views : 23571
Refbacks
- There are currently no refbacks.
Copyright (c) 2013 IJCCS - Indonesian Journal of Computing and Cybernetics Systems
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1