TEKNIK CLUSTERING DENGAN ALGORITMA K-MEDOIDS UNTUK MENANGANI STRATEGI PROMOSI DI POLITEKNIK TEDC BANDUNG

  • Novita Lestari Anggreini Politeknik TEDC Bandung
Keywords: K-Medoids, Promotion, Clustering

Abstract

This research was made based on promotional data that was conducted in 2015-2017. The unequal distribution of information about Politeknik TEDC Bandung can be seen in the data collected by two methods, first direct interviews to students from 2015-2017 and data taken at the time the student enrolled at Politeknik TEDC Bandung. In 2015-2017 there were several promotions carried out by Politeknik TEDC Bandung, including through Tribun Jabar newspaper, promotions to several educational institutions, installing billboards at several places, giving information about Politeknik TEDC Bandung on website, collaboration with other collages through alumni who work in industry or in schools, and last government scholarship programs. Unequal distribution of information to prospective students is one of the main problems of this research being made so that the promotions that have been carried out so far can be more efficient and effective. To be able to carry out promotions that are more effective and efficient, the researcher will process all data to produce a better promotional strategy. Data processing is carried out by grouping using the KMedoids algorithm on the data of prospective students including Name, Place and Date of Birth, Address, Religion, Telephone Number, School Origin, Selection of Study Programs and who provide information. Which data is obtained from promotion activities carried out from 2015 until 2017. After cleaning and repairing the data, a detailed and more specific dataset can be obtained for grouping the data. If necessary, there is a possibility that some data will be discarded to be more effective in testing the clustering process. Clustering results are made in the form of a chart bar which shows that the most registrants at the TEDC Polytechnic in Bandung are from West Java Province, while the City of Sumedang is the least registrant.

References

Asroni, Ronald Adrian. 2015. "Penerapan Metode K-Means Untuk Clustering Mahasiswa Berdasarkan Nilai Akademik Dengan Weka Interface Studi Kasus Pada Jurusan Teknik Informatika UMM Magelang". Semesta Teknika. pp: 76-82.

C. Zhang dan F. Fang, 2015. "An Improved K-means Clustering Algorithm". Journal of International & Computational Science. pp.193-199.

Miswaningsih, N., & Insani, N. 2016. "Analisis Perilaku Pengguna E-Learning BESMART Melalui Teknik Clustering dengan Algoritma K-Means". Yogyakarta: ISBN 978-602-73403-0-5. pp: 241-246.

Ong, J. O. 2013. "Implementasi Algoritma K-Means Clustering Untuk Menentukan Strategi Marketing President University". Jurnal Ilmiah Teknik Industri. Bekasi: ISSN 1412-6869. pp: 11-20.

J. Han, M. Kamber, & J. Pei. 2006. Data Mining: Concept and Techniques. Second Edition. Waltham: Morgan Kaufmann Publishers.

Kusrini dan Lutfi, E.T. 2009. "Algoritma Data Mining". Yogyakarta: Andi Offset.

Lamb, Hair, dan McDaniel. 2001. "Pemasaran". Buku1. Penerjemah David Octarevia. Jakarta: Penerbit Salemba Empat.

Larose, D.T. 2005. Discovering Knowledge in Data: An Introduction to Data mining. New Jersey: John Willey & Sons. Inc.

Moekijat. 2000. "Manajemen Pemasaran". Bandung: Penerbit Mandar Maju.

Santosa, B. 2007. "Data Mining: Teknik Pemanfaatan Data untuk Keperluan Bisnis". Yogyakarta: Graha Ilmu.

Nugraha, D. D., Naimah, Z., Fahmi, M., & Setiani, N. 2016. "Klasterisasi Judul Buku dengan Menggunakan Metode K-Means". Seminar Nasional Aplikasi Teknologi Informasi (SNATI). Yogyakarta: ISSN: 1907 - 5022. pp: G1-G4.

Dony Novaliendry, Yeka Hendriyani, ChengHong Yang, Hafilah Hamimi, “The Optimized K-Means Clustering Algorithms To Analyzed the Budget Revenue Expenditure in Padang”, Proceeding of the Electrical Engineering Computer Science and Informatics, vol. 2, no. 1, pp. 35-40, 2015

Published
2019-12-11
How to Cite
[1]
N. Anggreini, “TEKNIK CLUSTERING DENGAN ALGORITMA K-MEDOIDS UNTUK MENANGANI STRATEGI PROMOSI DI POLITEKNIK TEDC BANDUNG”, JTIP, vol. 12, no. 2, pp. 1-7, Dec. 2019.
Abstract viewed = 2002 times
PDF downloaded = 1548 times