Penentuan Penerima Beasiswa PPA Usulan dengan Menerapkan K-Means Clustering dan Multi Attribute Utility Theory Method - Mesran, Mesran (TIN: Terapan Informatika Nusantara, 2024)
The government launched the Proposed Academic Achievement Scholarship (PPA) Program to help outstanding students. This scholarship is open to diploma and undergraduate students throughout Indonesia, both at state and private universities. Recommendations for this scholarship can be submitted by various parties, such as universities, government, NGOs, or organizations/companies. Apart from that, this program still has several shortcomings in its distribution, such as the lack of accurate data regarding the economic conditions and academic achievements of students which hinders the process of determining scholarship recipients who are on target, the large number of applicants and the manual process used takes a long time and has the potential to cause errors. This causes delays in distributing scholarships to recipients, in addition to the lack of clear and measurable criteria in the assessment process opening up opportunities for nepotism and favoritism, so that scholarship recipients are not always the most capable. To overcome this problem, data mining techniques are used, namely K-Means Clustering and decision making using the MAUT method. The reason this research combines the two methods is because K-Means groups students with similar GPA values, so that less data is selected using MAUT. There are 5 criteria used to select scholarship recipients including GPA, parents' income, academic achievement, non-academic achievement and ethics. This research sample took 150 students. Data was collected using K-Means Clustering into two clusters with the final centroid value of Cluster_0: 3,636 (79 data) and Cluster_1: 2,897 (71 data). Cluster_0 was chosen for the next process because it has a higher centroid value. As a result of selection using the MAUT method, 15 students were declared entitled to receive scholarships. The student with the highest final score was Monalisa Marbun (0.583) and the lowest (15th place) was Jonathan Panca S P Gultom (0.386).