Analisis Kombinasi Algoritma K-Means Clustering dan TOPSIS Untuk Menentukan Pendekatan Strategi Marketing Berdasarkan Background Target Audiens
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Nurus Sarifatul Ngaeni
*
Universitas AMIKOM, Yogyakarta,
Indonesia
- Kusrini Kusrini Universitas AMIKOM, Yogyakarta, Indonesia
- Kusnawi Kusnawi Universitas AMIKOM, Yogyakarta, Indonesia (*) Corresponding Author
Abstract
The promotion is an annual agenda for STIMIK Tunas Bangsa Banjarnegara. The aim of this promotional activity is to attract more new students every year. On the other hand, campus promotion encounters obstacles in mapping applicant data from previous years so that considerations for new promotion policies are based on data from the school of origin of alumni or students. By using the K-Means Clustering algorithm, applicant data can be grouped according to the background represented through the school origin attribute. , parents' occupation and place of origin. Then the data is processed using DSS with the TOPSIS method to obtain priority references for marketing types for each cluster. The results of calculating the silhouette coefficient value for the five clusters obtained a score of 0.426. Meanwhile, in the ranking process using the TOPSIS method, the first rank was found in cluster 0 with a score of 0.994110. Further stages use the Decision Tree method to obtain output in the form of recommendations for promotion types for each cluster. For example, cluster 0 is recommended to use promotion types with codes P1, P2, P3, P8 and P9.
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