IMPLEMENTATION OF K-MEANS ALGORITHM FOR KNOWING THE STUDENT DISTRIBUTION PATTERN AS ADDITIONAL INFORMATION FOR UNIVERSITY PROMOTION
(Case Study: Students of Faculty of Teacher Training and Education University of Bengkulu)
Email : firstname.lastname@example.org
Every year Faculty of Teacher Training and Education University of Bengkulu (FKIP UNIB) accepts new students. The acceptance adds database year after year. The collection of student data can give a useful information for the faculty or universty, for example is the pattern of student data. That is why data has to be processed. An example of processing data is clustering data by using k-means algorithm. This research has aim to get useful information about student pattern in FKIP UNIB, and to apply k-means algorithm in clustering data. This research uses Delphi 7 as software, sequential linear as develompent method, software analisis and design uses Unified Modeling Language (UML). This research shows that 57 % stuedents of FKIP UNIB are from Bengkulu city and the rest are from the other province, 91% FKIP UNIB students graduate with IPK > 3.00, and 74% students can graduate the studying in 48-54 months. The biggest cluster of this research was 42% with IPK ≥ 3.00, graduate the studying in 48-54 months and come from Bengkulu city and another province.
K-means, Sekuensial Linier, Delphi 7, Unified Modeling Language (UML).