Pengelompokan Siswa Layak Penerima Beasiswa dengan Menerapkan Algoritma K-Means Clustering Data Mining
DOI:
https://doi.org/10.30865/mib.v8i2.7394Keywords:
Grouping, Student, Recipient, Scholarship, Data Mining, K-Means AlgorithmAbstract
Scholarships are financial assistance given to individuals with the aim of assisting them in financing the education they are pursuing. To overcome the gap between upper middle economic communities and lower middle economic communities in obtaining quality education. The aim of this program is to provide opportunities for financially disadvantaged students to experience quality education. In the process that occurs in awarding scholarships, there should be a strong basic foundation in the process of determining and making decisions that occur. Where the process of providing scholarships carried out so far should not be given to students who truly deserve it. The problem or impact that occurs from this is that the scholarship program does not run in accordance with the program's objectives, namely helping in the economic gap for students. One way that can be used to resolve this problem is to review previous recipient data. Data mining is a process of re-excavating data. Excavation is carried out by reviewing all the information contained in the data. In this research, the cluster analysis method is used, which is a multivariate technique used to group objects based on their characteristics. Clustering is the process of grouping data. Where the grouping process carried out on data is a grouping that does not yet have a class target or is called unsupervised learning. The results obtained in the research show that there are 2 clusters from the application of the K-Means algorithm. In cluster 1 there are 6 students in it and in cluster 2 there are 4 students in it.References
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