Alhamdulillah dalam jangka waktu kurang lebih 1 tahun, aku berhasil menyelesaikan skripsiku. Good bye..:) for ANFIS: Adaptive Neuro Fuzzy Inference System. Memikirkanmu benar-benar menyita tenaga dan pikiran. Luar biasa memerlukan energi ekstra. Karena masih jarang skripsi yang membahas ini. Bahkan di Universitasku baru aku yang mengangkat ANFIS. ANFIS kuambil karena dosenku S3 mengangkat ANFIS ini. Selain itu refensi bahasa Indonesia sedikit sekali. But aku tetap berusa hingga akhirnya selesai dengan baik. Oleh itu aku akan berusaha share, membantu kawan yang sedang mengalami kondisi yang pernah kualami ini. Silakan kalau ada yang ditanyakan seputar ANFIS.
Kalau mau download skripsiku dan contoh perhitungan ANFIS silakan KLIK DISINI.
Judul : Anlisis Data Runtun Waktu dengan Metode ANFIS : Adaptive Neuro Fuzzy Inference System (ANFIS)
Title : Time Series Data Analysis with Adaptive Neuro Fuzzy Inference System (ANFIS) Method
Abstrak
Salah satu metode analisis data runtun waktu yang populer adalah ARIMA. Metode ARIMA mensyaratkan beberapa asumsi antara lain residual model white noise, berdistribusi normal dan varian konstan. Model ARIMA cenderung lebih baik untuk data runtun waktu yang linier. Sedangkan untuk data runtun waktu nonlinier telah banyak dikaji dengan metode nonlinier, salah satunya adalah Adaptive Neuro Fuzzy Inference System atau ANFIS. Metode ANFIS adalah metode yang mengkombinasikan teknik Neural Network dan Fuzzy Logic. Dalam Tugas Akhir ini dibahas secara khusus mengenai metode ANFIS untuk analisis data runtun waktu yang mempunyai karakteristik antara lain stasioner, stasioner dengan outlier, nonstasioner dan nonstasioner dengan outlier, dan digunakan data harga minyak kelapa sawit Indonesia sebagai studi kasus. Hasil ANFIS yang diperoleh kemudian dibandingkan dengan hasil metode ARIMA berdasarkan nilai RMSE. Berdasarkan analisis dan pembahasan diperoleh bahwa hasil metode ANFIS lebih baik daripada metode ARIMA.
Kata kunci : ANFIS, ARIMA, data runtun waktu, nonstasioner, outlier
Abstract
One popular method of time series analysis is ARIMA. The ARIMA method requires some assumptions; residual of model must be white noise, normal distribution and constant variance. The ARIMA model tends to be better for time series data which is linear. Whereas for the nonlinear time series data have been widely studied by nonlinear methods, one of that is Adaptive Neuro Fuzzy Inference System or ANFIS. The ANFIS method is a method that combines techniques Neural Network and Fuzzy Logic. In this thesis discussed the ANFIS method specifically for the analysis of time series data that have characteristics such as stationary, stationary with outlier, non stationary and non stationary with outlier, and the data of Indonesian palm oil prices is used as a case study. The ANFIS results which were obtained are compared with the results of ARIMA method by the value of RMSE. Based on the analysis and discussion, it is obtained that the results of ANFIS method are better than the results of ARIMA method.
Keywords : ANFIS, ARIMA, time series data, non stasionary, outlier
Literature
1. Abiyev, R dkk. 2005. Electricity Consumption Prediction Model using Neuro-Fuzzy System. World Academy of Science, Engineering and Technology 8. Hal 128-131.
2. Agung, IGN. 2009. Time Series Data Analysis Using Eviews. Singapura: John Wiley & Sons (Asia) Pte Ltd.
3. Alakhras, MNY. 2005. Neural Network-based Fuzzy Inference System for Exchange Rate Prediction. Journal of Computer Science (Special Issue). Hal 112-120. Amman, Jordan.
4. Aldrian, E dan Yudha, SD. 2008. Application of Multivariate Anfis for Daily Rainfall Prediction: Influences Of Training Data Size. Makara, Sains Volume 12 No 1. Hal 7-14.
5. Alizadeh, M., dkk. 2009. Forecasting Exchange Rates: A Neuro-Fuzzy Approach. IFSA-EUSFLAT. Hal 1745-1750.
6. Atsalakis, GS, dkk. Probability of trend prediction of exchange rate by ANFIS. Recent Advances in Stochastic Modeling and Data Analysis. Hal 414-422.
7. Azadeh, A, dkk. 2009. A hybrid simulation-adaptive network based fuzzy inference system for improvement of electricity consumption estimation. Expert Systems with Applications 36(8). Hal 11108-11117.
8. Baseri, H dan Alinejad G. 2011. ANFIS Modeling of the Surface Roughness in Grinding Process. World Academy of Science, Engineering and Technology 73. Hal 499-503.
9. Fahimifard, SM dkk. 2009. Comparison of ANFIS, ANN, GARCH and ARIMA Techniques to Exchange Rate Forecasting. Journal of Applied Science 9(20). Hal 3541-3651.
10. Fausett, L. 1994. Fundamentals of Neural Networks Architectures, Algorithms, and Applications. New Jersey: Prentice Hall.
11. Jang, JSR. 1993. ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on System, Man, and Cybernetics Volume 23. Hal 665-685.
12. Jang, JSR., CT Sun, dan E Mizutani. 1997. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. London: Prentice-Hall, Inc.
13. Kablan, A. 2009. Adaptive Neuro-Fuzzy Inference System for Financial Trading using Intraday Seasonality Observation Model. World Academy of Science, Engineering and Technology 58. Hal 479-488.
14. Kusumadewi, S. 2003. Artificial Intelligence Teknik dan Aplikasinya. Yogyakarta: Graha Ilmu.
15. Kusumadewi, S dan Hartati S. 2006. Neuro Fuzzy: Integrasi Sistem Fuzzy & Jaringan Syaraf. Yogyakarta: Graha Ilmu.
16. Makridakis, S dkk. 1992. Metode dan Aplikasi Peramalan Edisi Kedua Jilid 1. Terjemahan oleh Untung S Andriyanto. Jakarta: Penerbit Erlangga.
17. Matlab. 1999. Fuzzy Logic Toolbox User’s Guide. The MathWorks, Inc.
18. Mordjaoui, M and Boudjema B. 2011. Forecasting and Modelling Electricity Demand Using Anfis Predictor. Journal of Mathematics and Statistics 7 (4). Hal 275-281.
19. Nayak, PC, dkk. 2004. A Neuro-Fuzzy Computing Technique for Modeling Hydrological Time Series. Journal of Hydrology. Hal 52-56.
20. Osowski, S dan Linh, TH. 2004. Neuro-Fuzzy TSK Network for Approximation.
21. Ross, TJ. 2010. Fuzzy Logic with Engineering Applications, Third Edition. Singapore: John Wiley & Sons, Inc.
22. Soejoeti, Z. 1987. Buku Materi Pokok Analisis Runtun Waktu. Jakarta: Penerbit Karunika, Universitas Terbuka.
23. Suhartono. 2008. Analisis Data Statistik dengan R. Surabaya: Jurusan Statistika ITS.
24. Warsito, B dan Ispriyanti D. 2004. Uji Linearitas Data Time Series dengan RESET Test. Jurnal Matematika dan Komputer Volume 7 Nomor 3. Hal 36-44.
25. Warsito, B. 2009. Kapita Selekta Statistika Neural Network. Semarang: BP Undip.
26. Wei, LY. 2011. An Expanded Adaptive Neuro-Fuzzy Inference System (ANFIS) Model Based on AR and Causality of Multination Stock Market Volatility for TAIEX Forecasting. African Journal of Business Management Vol. 5(15). Hal 6377-6387.
27. Wei, WWS. 2006. Time Series Analysis Univariate and Multivariate Methods Second Edition. USA: Pearson Education, Inc.
28. Yilmaz, NAS. 2003. A Temporal Neuro-Fuzzy Approach for Time Series Analysis. The Department of Computer Engineering, The Middle East Technical University.
Selengkapnya...