• Tidak ada hasil yang ditemukan

Analisis Algoritma C4.5 dan Fuzzy Sugeno untuk Optimasi Rule Base Fuzzy

N/A
N/A
Protected

Academic year: 2017

Membagikan "Analisis Algoritma C4.5 dan Fuzzy Sugeno untuk Optimasi Rule Base Fuzzy"

Copied!
2
0
0

Teks penuh

(1)

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 interpretability-oriented 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.

(2)

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.

Referensi

Dokumen terkait

(e) clipped center of gravity 15 Gambar 2.9 Diagram Blok Sistem Inferensi Fuzzy 17 Gambar 3.1 Alur Kerja Fuzzy Inference System 24 Gambar 3.2 Langkah:langkah Metode Sugeno 25 Gambar

Puji dan syukur kehadirat Allah Swt karena atas rahmat dan karuniaNya penulis dapat menyelesaikan tesis yang berjudul “Analisis Rule Fuzzy Inferensi Sugeno Dalam Sistem

Penelitian ini bertujuan untuk menganalisis Galat fungsi keanggotaan fuzzy pada metode mamdani dan metode sugeno untuk mendapatkan nilai optimasi fungsi dengan cepat

Penelitian ini bertujuan untuk menganalisis Galat fungsi keanggotaan fuzzy pada metode mamdani dan metode sugeno untuk mendapatkan nilai optimasi fungsi dengan

Hasil uji dari 30 data sampel uji algoritma fuzzy logic sugeno menunjukan bahwa tingkat keberhasilan menentukan prilaku NPC sebesar 100% pada permainan Battle

Dalam penelitian ini akan menggunakan metode Neural Network Backpropagation dan metode Fuzzy Logic Mamdani untuk memprediksi tingkat inflasi bulanan di Indonesia,

Dari hasil analisis perhitungan menggunakan metode perhitungan klasik, fuzzy mamdani dan sugeno, maka metode sugeno lebih mendekati nilai dari data senter dengan

Table 1: Some fuzzy logic systems References Fuzzy set Membership function Inference engine Defuzzification [6] Type-1 Triangular Mamdani Centroid of Gravity [118] Type-1 Triangular