RECOMMENDATION SYSTEM FOR DETERMINING MARKETING LOCATION FOR NEW STUDENTS BASED ON K-MEAN CLUSTERING

Data on new student admissions is often not utilized, especially at STIKES Widyagama Husada Malang. In fact, this data could be very useful for developing marketing strategies, especially in determining marketing locations for the potential to increase the number of prospective new students in a more targeted and efficient manner. In this research, the implementation of the K-Means Clustering algorithm was chosen to solve the problem of the data mining process for new student admissions. The data in this study uses data on new student admissions for 2020–2023, totaling 375 registrant data with attributes such as gender, age, intended study program, school of origin, major of origin, wave of registration, total registration history, and selected registration information. 3 clusters were produced with dominance in the 2nd cluster; the highest number of new student admissions was 103 registrants, with the highest regional percentage of 57%, namely Kab. Malang (57%), followed by Kota Malang (18%). Meanwhile, the most popular study program is S1 Ilmu Keperawatan (48%). Based on this research, a pattern was produced that can be used as a source of new information for higher education institutions, which can be used to support location decisions in an efficient and targeted marketing strategy for new students, namely in Kab. Malang, so universities can promote the most popular study program, namely the Bachelor of Science in Nursing study program, as an effort to increase the number of prospective new student applicants in the coming year.