Algoritma K-Means Dengan Menggunakan Metode Pengukuran Jarak dan Densitas Untuk Peningkatan Strategi Promosi Penerimaan Mahasiswa Baru (PMB)

  • riana safitri STMIK Widya Utama

Abstract

In a company the activity of promoting a product or service is the most needed aspect to support success, so promotion is one way for companies or organizations to develop, maintain, and advance the business that is being managed. In order for the promotion to be carried out in accordance with the expected target, it is necessary to establish market segmentation in which there are many targeted targets, because the targeted target is very dispersed and scattered and varied in the demands of their needs and desires. Data mining is an activity in the database process to find certain previously unknown / hidden patterns that are useful and meaningful and can be processed. One algorithm that can be used for clustering is the k-means algorithm. In order to maximize the k-means algorithm, we use distance and density measurement methods. Grouping data using k-means is done by calculating the closest distance from a data to a centroid point. In this study, the method of calculating the distance on k-means between Euclidean and density will be combined. The test will be carried out using the execution time and the bouldin index davies. From the tests that have been done, the calculation of eulidean distance and density has the most efficient accumulation of time and the value of Davies Index.

References

[1] L. E. I. Xu, C. Jiang, and J. Wang, “Information Security in Big Data : Privacy and Data Mining,” pp. 1149–1176, 2014.
[2] Lubis, Arlina , "Strategi Pemasaran dalam Persaingan Bisnis", 2014
[3] M. Verma, M. Srivastava, N. Chack, A. K. Diswar, and N. Gupta, “A Comparative Study of Various Clustering Algorithms in Data Mining Manish Verma , Mauly Srivastava , Neha Chack , Atul Kumar Diswar , Nidhi Gupta,” vol. 2, no. 3, pp. 1379–1384, 2012.
[4] P. Thangaraju, B. Deepa, and T. Karthikeyan, “Comparison of Data mining Techniques for Forecasting Diabetes Mellitus,” vol. 3, no. 8, pp. 7674–7677, 2014.
[6] L. Xu, C. Jiang, J. Wang, J. Yuan and Y. Ren, "Information Security in Big Data: Privacy and Data Mining," IEEE Access: The Journal for Rapid Open Access Publishing, vol. 1, pp. 1149-1176, 9 October 2014
[7] Purwanto, 2008
[8] N. C. a. J. Saichon, "Opinion Mining for Thai Restaurant Reviews using KMeans Clustering and MRF Feature Selection," Knowledge and Smart Technology (KST), 2015.
[9] S. Ghosh, “Comparative Analysis of K-Means and Fuzzy C- Means Algorithms,” vol. 4, no. 4, pp. 35–39, 2013.
[10] C. Zhang and Z. Fang, “An Improved K-means Clustering Algorithm Traditional K-mean Algorithm,” vol. 1, pp. 193–199, 2013.
[11] Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining. [Morgan Kaufmann series in data management systems]. https://doi.org/10.1002/1521-3773(20010316)40:6<9823::AID-ANIE9823>3.3.CO;2-C
[12] Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., … Dan, J. H. (2008). Top 10 algorithms in data mining. https://doi.org/10.1007/s10115-007-0114-2
[13] Zhang, C., & Fang, Z. (2013). An Improved K-means Clustering Algorithm Traditional K-mean Algorithm, 1, 193–199.
[14] J. Pengembangan, T. Informasi, and D. Ilmu, “Implementasi Metode Improved K-Means Untuk Mengelompokkan Dokumen Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer,” no. February, 2018.
[15] Informatika, J. I., Julianto, V., Permadi, J., Informatika, J. T., Negeri, P., Laut, T., … Laut, T. (2017). Baru Politeknik Negeri Tanah Laut Menggunakan Metode K-Means Clustering, 2(1), 99–105.
[16] Abdurasyid, M., Indriati, I., & Perdana, R. S. (2018). Implementasi Metode Improved K-Means Untuk Mengelompokkan Dokumen Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 2(10), 3939–3947. Retrieved from http://j-ptiik.ub.ac.id
[17] Xiong, C., & Lv, K. (2016). An Improved K-means text clustering algorithm By Optimizing initial cluster centers, 272–275. https://doi.org/10.1109/CCBD.2016.29
[18] Rahman, M. A., Islam, M. Z., & Bossomaier, T. (2015). ModEx and Seed-Detective: Two novel techniques for high quality clustering by using good initial seeds in K-Means. Journal of King Saud University - Computer and Information Sciences, 27(2), 113–128. https://doi.org/10.1016/j.jksuci.2014.04.002
Published
2021-03-22
How to Cite
SAFITRI, riana. Algoritma K-Means Dengan Menggunakan Metode Pengukuran Jarak dan Densitas Untuk Peningkatan Strategi Promosi Penerimaan Mahasiswa Baru (PMB). Teknikom: Teknologi Informasi, Ilmu Komputer dan Manajemen, [S.l.], v. 3, n. 2, p. 41-46, mar. 2021. ISSN 2598-2958. Available at: <https://journal.swu.ac.id/index.php/teknikom/article/view/162>. Date accessed: 13 may 2021.