Optimasi Support Vector Mechine (SVM) Menggunakan K-Means dan K-Medoids untuk Klasterisasi Tema Tugas Akhir
Abstract
The large amount of final project document data from study programs at the Sekolah Tinggi Manajemen Informatika dan Komputer (STMIK) Abulyatama can make a major contribution to the difficulty of the process of grouping a student's final project theme. The clustering process that has been carried out manually so far has been very ineffective and inefficient, so a data mining application is needed to manage the data, especially for clustering the data. The goal to be achieved from writing this thesis is to implement the Support Vector Machine with K-Means and K-Medoids to optimize the final assignment clustering. the results of the Optimization Support Vector Machine (SVM) analysis using K-Means and K-Medoids for Grouping Student Final Project Themes can be concluded in a number of ways, namely; with the K-Means Clustering method it can be seen that there are 23 data mining, 10 networks, 26 artificial intelligence, and 21 websites, and website 11 items.
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Sekolah Tinggi Manajemen Informatika Dan Komputer (STMIK) Abulyatama Aceh, Kota Banda Aceh, Provinsi Aceh, Indonesia
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Total: 18 References- Han, Jiawei dan Kamber, Micheline. (2016). Data Mining : Concept and. Techniques Second Edition. Morgan Kaufmann Publishers.
- Larose, Daniel T. (2015). Discovering Knowledge in Data : An Introduction to Data Mining. John Willey & Sons, Inc.
- Delen, D., & Crossland, M. D. (2008). Seeding the survey and analysis of research literature with text mining. Expert Systems with Applications, 34(3), 1707-1720. DOI: https://doi.org/10.1016/j.eswa.2007.01.035.
- Kontostathis, A., Galitsky, L. M., Pottenger, W. M., Roy, S., & Phelps, D. J. (2004). A survey of emerging trend detection in textual data mining. Survey of text mining, 185-224. DOI: https://doi.org/10.1007/978-1-4757-4305-0_9.
- Chakrabarti, S. (2000). Data mining for hypertext: A tutorial survey. ACM SIGKDD Explorations Newsletter, 1(2), 1-11. DOI: https://doi.org/10.1016/S0167-9236(99)00038-X.
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