Analisis Algoritma C4.5 dan Fuzzy Sugeno untuk Optimasi Rule Base Fuzzy
DAFTAR PUSTAKA
Aggarwal, C. C. (2015). Data mining: the textbook. Springer.
Agrawal, G. L., & Gupta, H. (2013). Optimization of C4. 5 Decision Tree Algorithm
for Data Mining Application. International Journal of Emerging Technology
and Advanced Engineering, 3(3), 341-345.
Anikin, I. V., & Zinoviev, I. P. (2015, May). Fuzzy control based on new type of
Takagi-Sugeno fuzzy inference system. In Control and Communications
(SIBCON), 2015 International Siberian Conference on (pp. 1-4). IEEE.
Bhargava, N., Sharma, G., Bhargava, R., & Mathuria, M. (2013). Decision tree
analysis on j48 algorithm for data mining. Proceedings of International
Journal of Advanced Research in Computer Science and Software
Engineering, 3(6).
Branson, J. S., & Lilly, J. H. (2001). Incorporation, characterization, and conversion
of negative rules into fuzzy inference systems. IEEE Transactions on fuzzy
systems, 9(2), 253-268.
Dai, W., & Ji, W. (2014). A mapreduce implementation of C4. 5 decision tree
algorithm. International Journal of Database Theory and Application, 7(1),
49-60.
Dewi, E.M. 2012. Linear VS Non-Linear. erlindamettadewi-fst09.web.unair.ac.id, 21
September 2012 (diakses 2 Agustus 2016).
Guillaume, Serge. "Designing fuzzy inference systems from data: An interpretabilityoriented review." IEEE Transactions on fuzzy systems 9.3 (2001): 426-443.
Gorunescu, F. (2011). Data Mining: Concepts, models and techniques (Vol. 12).
Springer Science & Business Media.
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
Telecommunications, 2, 56-60.
Kadi, I., & Idri, A. (2015, December). A Decision Tree-Based Approach for
Cardiovascular Dysautonomias Diagnosis: A Case Study. In Computational
Intelligence, 2015 IEEE Symposium Series on (pp. 816-823). IEEE.
Kapitanova, K., Son, S. H., & Kang, K. D. (2012). Using fuzzy logic for robust event
detection in wireless sensor networks. Ad Hoc Networks, 10(4), 709-722.
Kusrini, E. T. L. (2009). Algoritma Data Mining. Yogyakarta: Andi Offset.
Universitas Sumatera Utara
Kusumadewi, S. Purnomo, Hari. 2010. Aplikasi Logika Fuzzy Untuk Pendukung
Keputusan.
Larose, D. T. (2014). Discovering knowledge in data: an introduction to data mining.
John Wiley & Sons.
Lavanya, D., & Rani, K. U. (2011). Performance evaluation of decision tree classifiers
on medical datasets. International Journal of Computer Applications, 26(4).
Lilly, J. H. (2011). Fuzzy control and identification. John Wiley & Sons.
Peranginangin, R. (2016). Analisis Tingkat Akurasi Model Fuzzy Inferensi Sugeno dan
Tsukamoto Dalam Memprediksi Laju Inflasi Sumatera Utara (Master's thesis).
Qiao, X., Li, Z., Lu, W., & Liu, X. (2014, July). Data-based fuzzy rules extraction
method for classification. In Fuzzy Systems (FUZZ-IEEE), 2014 IEEE
International Conference on (pp. 260-266). IEEE.
Ross, T. J. (2010). Logic and Fuzzy Systems. Fuzzy Logic with Engineering
Applications, Third Edition, 117-173.
Siddique, N. (2013). Intelligent control: a hybrid approach based on fuzzy logic,
neural networks and genetic algorithms (Vol. 517). Springer.
Singla, J. (2015, March). Comparative study of Mamdani-type and Sugeno-type fuzzy
inference systems for diagnosis of diabetes. In Computer Engineering and
Applications (ICACEA), 2015 International Conference on Advances in (pp.
517-522). IEEE.
Su, X., Shi, P., Wu, L., & Song, Y. D. (2013). A novel control design on discrete-time
Takagi Sugeno fuzzy systems with time-varying delays. IEEE Transactions
on Fuzzy Systems, 21(4), 655-671.
Zavala, A. H., & Nieto, O. C. (2012). Fuzzy hardware: A retrospective and analysis.
IEEE Transactions on Fuzzy Systems, 20(4), 623-635.
Universitas Sumatera Utara
Aggarwal, C. C. (2015). Data mining: the textbook. Springer.
Agrawal, G. L., & Gupta, H. (2013). Optimization of C4. 5 Decision Tree Algorithm
for Data Mining Application. International Journal of Emerging Technology
and Advanced Engineering, 3(3), 341-345.
Anikin, I. V., & Zinoviev, I. P. (2015, May). Fuzzy control based on new type of
Takagi-Sugeno fuzzy inference system. In Control and Communications
(SIBCON), 2015 International Siberian Conference on (pp. 1-4). IEEE.
Bhargava, N., Sharma, G., Bhargava, R., & Mathuria, M. (2013). Decision tree
analysis on j48 algorithm for data mining. Proceedings of International
Journal of Advanced Research in Computer Science and Software
Engineering, 3(6).
Branson, J. S., & Lilly, J. H. (2001). Incorporation, characterization, and conversion
of negative rules into fuzzy inference systems. IEEE Transactions on fuzzy
systems, 9(2), 253-268.
Dai, W., & Ji, W. (2014). A mapreduce implementation of C4. 5 decision tree
algorithm. International Journal of Database Theory and Application, 7(1),
49-60.
Dewi, E.M. 2012. Linear VS Non-Linear. erlindamettadewi-fst09.web.unair.ac.id, 21
September 2012 (diakses 2 Agustus 2016).
Guillaume, Serge. "Designing fuzzy inference systems from data: An interpretabilityoriented review." IEEE Transactions on fuzzy systems 9.3 (2001): 426-443.
Gorunescu, F. (2011). Data Mining: Concepts, models and techniques (Vol. 12).
Springer Science & Business Media.
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
Telecommunications, 2, 56-60.
Kadi, I., & Idri, A. (2015, December). A Decision Tree-Based Approach for
Cardiovascular Dysautonomias Diagnosis: A Case Study. In Computational
Intelligence, 2015 IEEE Symposium Series on (pp. 816-823). IEEE.
Kapitanova, K., Son, S. H., & Kang, K. D. (2012). Using fuzzy logic for robust event
detection in wireless sensor networks. Ad Hoc Networks, 10(4), 709-722.
Kusrini, E. T. L. (2009). Algoritma Data Mining. Yogyakarta: Andi Offset.
Universitas Sumatera Utara
Kusumadewi, S. Purnomo, Hari. 2010. Aplikasi Logika Fuzzy Untuk Pendukung
Keputusan.
Larose, D. T. (2014). Discovering knowledge in data: an introduction to data mining.
John Wiley & Sons.
Lavanya, D., & Rani, K. U. (2011). Performance evaluation of decision tree classifiers
on medical datasets. International Journal of Computer Applications, 26(4).
Lilly, J. H. (2011). Fuzzy control and identification. John Wiley & Sons.
Peranginangin, R. (2016). Analisis Tingkat Akurasi Model Fuzzy Inferensi Sugeno dan
Tsukamoto Dalam Memprediksi Laju Inflasi Sumatera Utara (Master's thesis).
Qiao, X., Li, Z., Lu, W., & Liu, X. (2014, July). Data-based fuzzy rules extraction
method for classification. In Fuzzy Systems (FUZZ-IEEE), 2014 IEEE
International Conference on (pp. 260-266). IEEE.
Ross, T. J. (2010). Logic and Fuzzy Systems. Fuzzy Logic with Engineering
Applications, Third Edition, 117-173.
Siddique, N. (2013). Intelligent control: a hybrid approach based on fuzzy logic,
neural networks and genetic algorithms (Vol. 517). Springer.
Singla, J. (2015, March). Comparative study of Mamdani-type and Sugeno-type fuzzy
inference systems for diagnosis of diabetes. In Computer Engineering and
Applications (ICACEA), 2015 International Conference on Advances in (pp.
517-522). IEEE.
Su, X., Shi, P., Wu, L., & Song, Y. D. (2013). A novel control design on discrete-time
Takagi Sugeno fuzzy systems with time-varying delays. IEEE Transactions
on Fuzzy Systems, 21(4), 655-671.
Zavala, A. H., & Nieto, O. C. (2012). Fuzzy hardware: A retrospective and analysis.
IEEE Transactions on Fuzzy Systems, 20(4), 623-635.
Universitas Sumatera Utara