Analisis Tingkat Akurasi Model Fuzzy Inferensi Sugeno dan Tsukamoto Dalam Memprediksi Laju Inflasi Sumatera Utara
DAFTAR PUSTAKA
Abadi, A.M. 2009. Pemodelan Data Fuzzy Time Series Dengan Menggunakan
Dekomposisi Nilai Singular Dan Aplikasinya Pada Perkiraan Tingkat Inflasi Di
Indonesia. Jurnal Penelitian Saintek14(1): 129-144.
Bandyopadhyay, S., Mistri, H., Chattopadhyay, P. & Maji, B. 2013. Antenna Array
Side Lobe Reduction by Implementing Non – Uniform Amplitude Using
Tsukamoto Fuzzy Logic Controller. International Journal of electronIcs &
Communication Technology.4(1): 54-57
Batra, G. & Trivedi, M. 2013. A fuzzy approach for software effort Estimation.
International Journal on Cybernetics & Informatics2(1): 9-15
Chan, L.K.C., Jegadeesh, N. & Lakonishok, J. 1997. Momentum Strategies. Journal
Of Finance51(5):1681-1713
Hosseinzadeh, B., Zareiforoush, H., Adabi, M.E. & Motevali, A. 2011. Development
of a Fuzzy Model to Determine the Optimum Shear Strength of Wheat Stem.
International Journal of Computer Science and Telecommunications2(4): 56-60.
Inflasi Kumulatif Sumut 8,17 Persen. 2014, Waspada.co.id, 02 Januari 2015 (Diakses
15 Januari 2015).
Iswari, L., & Wahid, F. 2005. Alat Bantu Sistem Inferensi Fuzzy Metode Sugeno
Orde Satu. Seminar Nasional Aplikasi Teknologi Informasi.pp.1-3.
Kamble, P.N,. 2013. FLC Modeling of Clasical EEG Signals Model by the Technique
Tsukamoto Fuzzy Rule Base. International Journal of Statistics and
Mathematics (7)3: 52-57
Kaur, A. & Kaur, A. 2012. Comparison of Mamdani-Type and Sugeno-Type Fuzzy
Inference Systems for Air Conditioning System . International Journal of Soft
Computing and Engineering2(2): 323-325.
Klir, G. J. & Yuan, B. 1995. Fuzzy Set And Fuzzy Logic Theory And Application.
Prentice-Hall Inc. United State of America
Mahaswari, T. & Asthana, A. 2013. Image Enhancement Using Fuzzy Technique.
International Journal Of Research Review In Engineering Science &
Technology.2(2): 1-4.
Mahmood, A.K. & Taha, H.H. 2013. Design Fuzzy Logic Controller for Liquid Level
Control. International Journal of Emerging Science and Engineering1(11):2326
Мashhadan, M.A.A. & Lobaty, A.A. 2013. Fuzzification Mode For Signal In
Nonlinear Stochastic Systems. International Journal of Information Technology,
Control and Automation3(1): 71-83.
Mayilvaganan, M. & Rajeswari, K. 2014. Risk Factor Analysis to Patient Based on
Fuzzy Logic Control System. International Journal of Engineering Research
and General Science.2(5): 1-6.
Nasr, A.S., Rezai, M. & Barmaki, M.D. 2012. Analysis of groundwater quality using
mamdani fuzzy inference system (mfis) in yazd province, iran. International
Journal of Computer Applications.59(7): 45-53.
Nasution, H. 2012. Implementasi Logika Fuzzy pada Sistem Kecerdasan Buatan.
Jurnal ELKHA4(2): 4-8.
Universitas Sumatera Utara
Nezhad, Q.A., Zand, J.P. & Hosaini, S.S. 2013. An investigation on fuzzy logic
controllers (takagi-sugeno & mamdani) in inverse pendulum system.
International Journal of Fuzzy Logic Systems3(3): 1-14.
Pengenalan Inflasi. 2014. bi.go.id, 23 September 2014 (diakses 30 September 2014)
Poonam., Tripathi, S.P., Shukla, K.P. 2012. Uncertainty Handling using Fuzzy Logic
in Rule Based Systems. International Journal of Advanced Science and
Technology45: 31 – 46.
Roubus, J.A., Setnes, M. & Abonyi, J. 2003. Learning Fuzzy Classification Rules
From Labeled Data. Information Sciences An International Journal1: 77-93.
Sahadudheen, I. & Scholar, M.P. 2012. A Cointegration And Error Correction
Approach To The Determinants Of Inflation In India. International Journal
Economy3(1): 105-112.
Saxena, N., Saxena, K.K. 2010. Fuzzy Logic Based Students Performance Analysis
Model for Educational Institutions. International Journal of Research1 :79 –
86.
Siji, P.D. & Rajesh, R. 2011. Takagi-Sugeno Fuzzy Modeling of Logistic Map using
Genetic Algorithm. International Journal of Wisdom Based Computing1(3): 913.
Sivarao., Brevern, P., El-Thayeb, N.S.M. & Vengkatesh, V.C. 2009. GUI Based
Mamdani Fuzzy Inference System Modeling To Predict Surface Roughness in
Laser Machining. International Journal of Electrical & Computer Sciences
IJECS-IJENS9(9): 37-43.
Sofwan, A. 2005. Penerapan Fuzzy Logic Pada Sistem Pengaturan Jumlah Air
Berdasarkan Suhu Dan Kelembaban. Seminar Nasional Aplikasi Teknologi
Informasi.pp. C-89-C-83.
Srinivas, P. & Rao, P.D.P. 2012. Comparative Analysis Of Conventional Pid
Controller And Fuzzy Controller With Various Defuzzification Methods In A
Three Tank Level Control System. International Journal of Information
Technology, Control and Automation.2(4): 75-86.
Suwandi., Irawan, M.I. & Muklhas, I. 2011. Aplikasi Sistem Inferensi Fuzzy Metode
Sugeno Dalam Memperkirakan Produksi Air Mineral Dalam Kemasan. Seminar
Nasional Penelitian, Pendidikan dan Penerapan MIPA.pp-M1-M9
Thamrin, F. 2012. Studi inferensi fuzzy tsukamoto untuk penentu faktor pembebanan
trafo pln. Tesis. Universitas Diponegoro Semarang.
Zadeh, L.A. 1965. Fuzzy Sets. Information and Control 8.338-353.
Zadeh, L.A. 1973. Outline Of A New Approach To The Analysis Ofcomplex Systems
And Decision Processes. IEEE Transactions on Systems, Man, and
Cybernetics3: 28–44.
Zadeh, L.A. 1990. Fuzzy Sets And Systems. International Journal of General
Systems.17(2): 129-138.
Zadeh, L.A. 2004. Fuzzy Logic Systems: Origin, Concepts, And Trends. Computer
Science Division Department of EECS UC Berkeley. 1-138.
Universitas Sumatera Utara
Abadi, A.M. 2009. Pemodelan Data Fuzzy Time Series Dengan Menggunakan
Dekomposisi Nilai Singular Dan Aplikasinya Pada Perkiraan Tingkat Inflasi Di
Indonesia. Jurnal Penelitian Saintek14(1): 129-144.
Bandyopadhyay, S., Mistri, H., Chattopadhyay, P. & Maji, B. 2013. Antenna Array
Side Lobe Reduction by Implementing Non – Uniform Amplitude Using
Tsukamoto Fuzzy Logic Controller. International Journal of electronIcs &
Communication Technology.4(1): 54-57
Batra, G. & Trivedi, M. 2013. A fuzzy approach for software effort Estimation.
International Journal on Cybernetics & Informatics2(1): 9-15
Chan, L.K.C., Jegadeesh, N. & Lakonishok, J. 1997. Momentum Strategies. Journal
Of Finance51(5):1681-1713
Hosseinzadeh, B., Zareiforoush, H., Adabi, M.E. & Motevali, A. 2011. Development
of a Fuzzy Model to Determine the Optimum Shear Strength of Wheat Stem.
International Journal of Computer Science and Telecommunications2(4): 56-60.
Inflasi Kumulatif Sumut 8,17 Persen. 2014, Waspada.co.id, 02 Januari 2015 (Diakses
15 Januari 2015).
Iswari, L., & Wahid, F. 2005. Alat Bantu Sistem Inferensi Fuzzy Metode Sugeno
Orde Satu. Seminar Nasional Aplikasi Teknologi Informasi.pp.1-3.
Kamble, P.N,. 2013. FLC Modeling of Clasical EEG Signals Model by the Technique
Tsukamoto Fuzzy Rule Base. International Journal of Statistics and
Mathematics (7)3: 52-57
Kaur, A. & Kaur, A. 2012. Comparison of Mamdani-Type and Sugeno-Type Fuzzy
Inference Systems for Air Conditioning System . International Journal of Soft
Computing and Engineering2(2): 323-325.
Klir, G. J. & Yuan, B. 1995. Fuzzy Set And Fuzzy Logic Theory And Application.
Prentice-Hall Inc. United State of America
Mahaswari, T. & Asthana, A. 2013. Image Enhancement Using Fuzzy Technique.
International Journal Of Research Review In Engineering Science &
Technology.2(2): 1-4.
Mahmood, A.K. & Taha, H.H. 2013. Design Fuzzy Logic Controller for Liquid Level
Control. International Journal of Emerging Science and Engineering1(11):2326
Мashhadan, M.A.A. & Lobaty, A.A. 2013. Fuzzification Mode For Signal In
Nonlinear Stochastic Systems. International Journal of Information Technology,
Control and Automation3(1): 71-83.
Mayilvaganan, M. & Rajeswari, K. 2014. Risk Factor Analysis to Patient Based on
Fuzzy Logic Control System. International Journal of Engineering Research
and General Science.2(5): 1-6.
Nasr, A.S., Rezai, M. & Barmaki, M.D. 2012. Analysis of groundwater quality using
mamdani fuzzy inference system (mfis) in yazd province, iran. International
Journal of Computer Applications.59(7): 45-53.
Nasution, H. 2012. Implementasi Logika Fuzzy pada Sistem Kecerdasan Buatan.
Jurnal ELKHA4(2): 4-8.
Universitas Sumatera Utara
Nezhad, Q.A., Zand, J.P. & Hosaini, S.S. 2013. An investigation on fuzzy logic
controllers (takagi-sugeno & mamdani) in inverse pendulum system.
International Journal of Fuzzy Logic Systems3(3): 1-14.
Pengenalan Inflasi. 2014. bi.go.id, 23 September 2014 (diakses 30 September 2014)
Poonam., Tripathi, S.P., Shukla, K.P. 2012. Uncertainty Handling using Fuzzy Logic
in Rule Based Systems. International Journal of Advanced Science and
Technology45: 31 – 46.
Roubus, J.A., Setnes, M. & Abonyi, J. 2003. Learning Fuzzy Classification Rules
From Labeled Data. Information Sciences An International Journal1: 77-93.
Sahadudheen, I. & Scholar, M.P. 2012. A Cointegration And Error Correction
Approach To The Determinants Of Inflation In India. International Journal
Economy3(1): 105-112.
Saxena, N., Saxena, K.K. 2010. Fuzzy Logic Based Students Performance Analysis
Model for Educational Institutions. International Journal of Research1 :79 –
86.
Siji, P.D. & Rajesh, R. 2011. Takagi-Sugeno Fuzzy Modeling of Logistic Map using
Genetic Algorithm. International Journal of Wisdom Based Computing1(3): 913.
Sivarao., Brevern, P., El-Thayeb, N.S.M. & Vengkatesh, V.C. 2009. GUI Based
Mamdani Fuzzy Inference System Modeling To Predict Surface Roughness in
Laser Machining. International Journal of Electrical & Computer Sciences
IJECS-IJENS9(9): 37-43.
Sofwan, A. 2005. Penerapan Fuzzy Logic Pada Sistem Pengaturan Jumlah Air
Berdasarkan Suhu Dan Kelembaban. Seminar Nasional Aplikasi Teknologi
Informasi.pp. C-89-C-83.
Srinivas, P. & Rao, P.D.P. 2012. Comparative Analysis Of Conventional Pid
Controller And Fuzzy Controller With Various Defuzzification Methods In A
Three Tank Level Control System. International Journal of Information
Technology, Control and Automation.2(4): 75-86.
Suwandi., Irawan, M.I. & Muklhas, I. 2011. Aplikasi Sistem Inferensi Fuzzy Metode
Sugeno Dalam Memperkirakan Produksi Air Mineral Dalam Kemasan. Seminar
Nasional Penelitian, Pendidikan dan Penerapan MIPA.pp-M1-M9
Thamrin, F. 2012. Studi inferensi fuzzy tsukamoto untuk penentu faktor pembebanan
trafo pln. Tesis. Universitas Diponegoro Semarang.
Zadeh, L.A. 1965. Fuzzy Sets. Information and Control 8.338-353.
Zadeh, L.A. 1973. Outline Of A New Approach To The Analysis Ofcomplex Systems
And Decision Processes. IEEE Transactions on Systems, Man, and
Cybernetics3: 28–44.
Zadeh, L.A. 1990. Fuzzy Sets And Systems. International Journal of General
Systems.17(2): 129-138.
Zadeh, L.A. 2004. Fuzzy Logic Systems: Origin, Concepts, And Trends. Computer
Science Division Department of EECS UC Berkeley. 1-138.
Universitas Sumatera Utara