Algoritma K-Means untuk Pengelompokan Topik Skripsi Mahasiswa


Muhammad Rafi Muttaqin(1*); Meriska Defriani(2);

(1) Sekolah Tinggi Teknologi Wastukancana
(2) Sekolah Tinggi Teknologi Wastukancana
(*) Corresponding Author

  

Abstract


In helping to develop technology in the field of education as well as bringing about a major change in competitiveness between individuals and groups, to be able to do so requires sufficient information and data to be analyzed further. In this case STT Wastukancana Purwakarta is under the auspices of Bunga Bangsa Foundation, seeing that STT Wastukancana Purwakarta students have several obstacles in their final project, one of which is difficult in determining the topic of the thesis title to be made so that sometimes the topic of the thesis title taken is not in accordance with their abilities each student. This problem can be overcome by applying the clustering method. The analytical method used is Knowledge Discovery in Database (KDD). The method of grouping students uses the clustering method and the K-Means algorithm as a clustering calculation where the Clustering aims to divide students into clusters based on grades obtained from semester 1 to 7, so as to produce student recommendations in taking thesis topics. The tool used to implement the algorithm is Rapidminer. The results of this study are grouping students according to their expertise, which is obtained based on the cluster that has the highest score and is dominated by the most subjects according to the subjects that have been grouped by each expertise. So, the results of this cluster are used as a reference for students to take the thesis title topic.

Keywords


Data Mining; Knowledge Discovery in Database; Clustering; K-Means; Rapidminer

  
  

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doi  https://doi.org/10.33096/ilkom.v12i2.542.121-129 		  

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References


V. K. Bakti and J. Indriyatno, “Klasterisasi Dokumen Tugas Akhir Menggunakan K-Means Clustering sebagai Analisa Penerapan Sistem Temu Kembali,” J. Ilm. Manaj. Inform. dan Komput., vol. 1, no. 1, pp. 31–34, 2017.

M. Sholehhudin, M. Fauzi Ali, and S. Adinugroho, “Implementasi Metode Text Mining dan K-Means Clustering untuk Pengelompokan Dokumen Skripsi ( Studi Kasus : Universitas Brawijaya ),” vol. 2, no. 11, pp. 5518–5524, 2018.

P. Bhatia, Data Mining and Data Warehousing : Principles and Practical Techniques. United Kingdom: Cambridge University Pres, 2019.

M. Feng et al., “Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data,” IEEE Access, vol. 7, pp. 106111–106123, 2019.

E. Turban, R. E. Sharda, and D. Delen, Decision Support Systems and Intelligent Systems (9th Edition). Prentice Hall, 2010.

S. Sharma and ShikhaRai, “Genetic K-Means Algorithm – Implementation and Analysis,” Int. J. Recent Technol. Eng., vol. 1, no. 2, pp. 117–120, 2012.

C.-P. Wei and I.-T. Chiu, “Approach, Turning telecommunications call details to churn prediction: a data mining,” Expert Syst. Appl., vol. 23, no. 2, pp. 103–112, 2002.

Suyanto, Data Mining Untuk Klasifikasi dan Klasterisasi. bandung: Informatika, 2017.

C. Yuan and H. Yang, “Research on K-Value Selection Method of K-Means Clustering Algorithm,” Multidisciplany Sci. J., vol. 16, no. 2, pp. 226–235, 2019.

O. Somantri, S. Wiyono, and Dairoh, “Metode K-Means untuk Optimasi Klasifikasi Tema Tugas Akhir Mahasiswa Menggunakan Support Vector Machine (SVM),” Sci. J. Informatics, vol. 3, no. 1, pp. 34–45, 2016.

I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed. San Francisco: Elsevier Inc, 2005.

M. Z. Rodriguez, C. H. Comin, D. C. O. M. Bruno, D. R. Amancio, and L. da F. C. F. A. Rodrigues, “Clustering algorithms: A comparative approach,” PLoS One, vol. 14, no. 1, pp. 1–34, 2019.

J. Han, M. Kamber, and J. Pei, Data Mining Concepts and Techniques Third Edition. USA: Elsevier Inc, 2012.

V. Gupta and G. Lehal, “A Survey of Text Mining Techniques and Applications,” J. Emerg. Technol. Web Intell., vol. 1, no. 1, pp. 60–76, 2009.

L. Rokach and O. Maimon, Data Mining with Decision Trees: Theory and Applications 2nd Ed. World Scientific Publishing Co., 2015.


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