IDENTIFIKASI TIGA JENIS BUNGA IRIS MENGGUNAKAN ANFIS
Keywords:
ANFIS, Classification, Fuzzy Logic, Iris identification, Neural networkAbstract
This paper was based on our research in preparing several tools for identification on various houseplants. One of tools we investigated was ANFIS. As a preliminary research, we tried to apply ANFIS to identify three kinds of Iris. As we know, many researches have used Iris data came from Fisher’s research in 1936 for classification purposes. In this research, we try to several kinds of parameters of fuzzy logic on input, especially on membership functions and number of function’s membership.
The result showed that the worst performance by using bell function was 70%. Not so good. Therefore, many explorations must be conducted to get better performance.
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References
Aik, L.E; Jayakumar, Y.; 2008; A Study of Neuro-fuzzy System in Approximation-based Problems; on MATEMATICA, Volume 24, Number 2, p. 113-130.
Alavala, C.R.; 2008; Fuzzy Logic and Neural Networks Basic Concepts & Applications; India: New Age International (P) Limited, Publishers.
Duda, R.O; Hart, P.E.; Stork, D.G.; 2001; Pattern Classification; New York: John Wiley & Sons.
Fu, L.; 1994; Neural Networks in Computer Intelligence; Singapore: McGraw-Hill Book Co.
Iyatomi, H; Hagiwara, M.; 2004; Adaptive Fuzzy Inference neural Network; on Pattern Recognition Society. No. 347; p. 2049-2057.
Shing, J; Jang, R.; 1993; ANFIS : Adap tive-Ne twork-Based Fuzzy
Silipo, R.; Berthold, M.R.; 2000; Input Features’ Impact on Fuzzy Decision Processes; IEEE Transactions On Systems, Man, And Cybernetics; http://www.inf.uni-konstanz.de/bioml2/publications/Papers2000/SiBe00_infogain_smcb/paper-smc-e-0-4-2.pdf.
The MathWorks; 2009; Fuzzy Logic Toolbox User’s Guide MATLAB; USA: The MathWorks, Inc.
Alavala, C.R.; 2008; Fuzzy Logic and Neural Networks Basic Concepts & Applications; India: New Age International (P) Limited, Publishers.
Duda, R.O; Hart, P.E.; Stork, D.G.; 2001; Pattern Classification; New York: John Wiley & Sons.
Fu, L.; 1994; Neural Networks in Computer Intelligence; Singapore: McGraw-Hill Book Co.
Iyatomi, H; Hagiwara, M.; 2004; Adaptive Fuzzy Inference neural Network; on Pattern Recognition Society. No. 347; p. 2049-2057.
Shing, J; Jang, R.; 1993; ANFIS : Adap tive-Ne twork-Based Fuzzy
Silipo, R.; Berthold, M.R.; 2000; Input Features’ Impact on Fuzzy Decision Processes; IEEE Transactions On Systems, Man, And Cybernetics; http://www.inf.uni-konstanz.de/bioml2/publications/Papers2000/SiBe00_infogain_smcb/paper-smc-e-0-4-2.pdf.
The MathWorks; 2009; Fuzzy Logic Toolbox User’s Guide MATLAB; USA: The MathWorks, Inc.
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Published
2010-06-30
How to Cite
Kadir, A. (2010). IDENTIFIKASI TIGA JENIS BUNGA IRIS MENGGUNAKAN ANFIS. Jurnal Teknologi, 3(1), 10–15. Retrieved from https://ejournal.akprind.ac.id/index.php/jurtek/article/view/835
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