Implementation of Fuzzy C-Means for Clustering the Majelis Ulama Indonesia (MUI) Fatwa Documents
DOI:
https://doi.org/10.15575/join.v6i1.591Keywords:
Fatwa, Clustering, Fuzzy C-MeansAbstract
Since the Indonesian Ulema Council (MUI) was established in 1975 until now, this institution has produced 201 edicts covering various fields. Text mining is one of the techniques used to collect data hidden from data that form text. One method of extracting text is Clustering. The present study implements the Fuzzy C-Means Clustering method in MUI fatwa documents to classify existing fatwas based on the similarity of the issues discussed. Silhouette Coefficient is used to analyze the resulting clusters, with the best value of 0.0982 with 10 clusters grouping. Classify fatwas based on the similarity of the issues discussed can make it easier and faster in the search for an Islamic law in Indonesia.
References
M. Allahyari, E. D. Trippe, and J. B. Gutierrez, “A Brief Survey of Text Mining : Classification , Clustering and Extraction Techniques,†2017.
W. B. Zulfikar, M. Irfan, C. N. Alam, and M. Indra, “The comparation of text mining with Naive Bayes classifier, nearest neighbor, and decision tree to detect Indonesian swear words on Twitter,†5th Int. Conf. Cyber IT Serv. Manag., vol. pp. 1-5, d, 2017.
D. S. Maylawati and G. A. P. Saptawati, “Set of Frequent Word Item sets as Feature Representation for Text with Indonesian Slang,†Int. Conf. Comput. Appl. Informatics 2016, 2017, doi: 10.1088/1742-6596/755/1/011001.
M. I. N. Saputra, D. Fauzy, R. A. Hakim, P. Dauni, M. D. Firdaus, and I. Taufik, “Implementation of Fuzzy C-Means algorithm to classifying research topics in informatics department, UIN Sunan Gunung,†J. Phys. Conf. Ser., vol. 1402, no. 2, 2019, doi: 10.1088/1742-6596/1402/2/022091.
B. Santosa, Data mining teknik pemanfaatan data untuk keperluan bisnis. Yogyakarta: Graha Ilmu, 2007.
and N. I. G. O. M. E. Zein, L. M. E. Bakrawy, “A robust 3D mesh watermarking algorithm utilizing fuzzy CMeans clustering,†Futur. Comput. Informatics, vol. 2, pp. 148–156, 2017.
N. P. and M. P. J. Iran, “Clustering Techniques and the Similarity used in Clustering: A survey,†Int. J. Comput. Appl., vol. 134, no. 7, 2016.
C. Slamet, “Clustering the Verses of the Holy Qur’an using K-Means Algorithm,†Asian J. Inf. Technol., vol. 15, no. 24, 2016.
D. D. C. Nugraha, “Klasterisasi Judul Buku dengan Menggunakan Metode K – Means,†Semin. Nas. Apl. Teknol. Inf., 2014.
E. Yulian, “Text Mining dengan K-Means Clustering pada Tema LGBT dalam Arsip Tweet Masyarakat Kota Bandung,†J. Mat. “MANTIK,†vol. 4, no. 1, 2018.
L. Agusta, U. Kristen, and S. Wacana, “Perbandingan Algoritma Stemming Porter Dengan Algoritma Nazief & Adriani Untuk Stemming Dokumen Teks Bahasa Indonesia,†pp. 196–201, 2009.
J. C. Dun, “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters,†J. Cybernet, vol. 3, no. 3, pp. 32–57, 1973.
J. C. Bezdek, Pattern recognition with fuzzy objective function algorithms. USA: Kluwer Academic Publishers Norwell, 1981.
E. T. Luthfi, “FUZZY C-MEANS UNTUK CLUSTERING DATA ( STUDI KASUS : DATA PERFORMANCE MENGAJAR DOSEN ),†vol. 2007, no. November, pp. 1–7, 2007.
S. Kusumadewi and H. Purnomo, Aplikasi Logika Fuzzy untuk pendukung keputusan. Yogyakarta: Graha Ilmu, 2010.
M. Zhang, L., & Luo, iverse fuzzy c-means for image clustering. Pattern Recognition Letters, 1st ed. 2018.
L. Zhang, M. Luo, J. Liu, Z. Li, and Q. Zheng, “Diverse Fuzzy c -Means for Image Clustering,†Pattern Recognit. Lett., 2018.
J. Stetco, A., Zeng, X., & Keane, “Expert Systems with Applications Fuzzy C-means ++ : Fuzzy C-means with effective seeding initialization,†Expert Syst. Appl., pp. 7541–7548, 2015.
Muhardi and Nisar, “PENENTUAN PENERIMA BEASISWA DENGAN ALGORITMA FUZZY C-MEANS DI UNIVERSITAS MEGOW PAK TULANG BAWANG,†J. TIM Darmajaya, vol. 01, no. 02, pp. 158–174, 2015.
J. O. Yang, Y., Pedersen, “Comparative Study on Feature Selection in Text Categorization,†Proc. Fourteenth Int. Conf. Mach. Learn., 1997.
H. Toyota, T., Nobuhara, “Visualization of the Internet News Based on Efficient Self-Organizing Map Using Restricted Region Search and Dimensionality Reduction,†J. Adv. Comput. Intell. Intell. Informatics, vol. 6, 2012.
M. Jujjuri, Ramadevi & Rao, “Evaluation of enhanced subspace clustering validity using silhouette coefficient internal measure,†J. Adv. Res. Dyn. Control Syst., pp. 321–328, 2019.
Downloads
Published
Issue
Section
Citation Check
License
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
-
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
-
NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.
-
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
- You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
- No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License