TEKNIK CLUSTERING DENGAN ALGORITMA K-MEDOIDS UNTUK MENANGANI STRATEGI PROMOSI DI POLITEKNIK TEDC BANDUNG - Novita Lestari Anggreini (Jurnal Teknologi Informasi dan Pendidikan, 2019)
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.