K-Medoid Algorithm in Clustering Student Scholarship Applicants
Abstract: Data Grouping
scholarship applicants Bantuan Belajar Mahasiswa (BBM) grouped into 3
categories entitled of students who are eligible to receive, be considered, and
not eligible to receive scholarship. Grouping into 3 groups is useful to make
it easier to determine the scholarship recipients fuel. K-Medoids algorithm is
an algorithm of clustering techniques based partitions. This technique can
group data is student scholarship applicants. The purpose of this study was to
measure the performance of the algorithm, this measurement in view of the
results of the cluster by calculating the value of purity (purity measure) of
each cluster is generated. The data used in this research is data of students
who apply for scholarships as many as 36 students. Data will be converted into
three datasets with different formats, namely the partial codification
attribute data, attributes and attribute the overall codification of the
original data. Value purity on the whole dataset of data codification greatest
value is 91.67%, it can be concluded that the K-Medoids algorithm is more
suitable for use in a dataset with attributes encoded format overall.
Penulis: Sofi Defiyanti,
Mohamad Jajuli, Nurul Rohmawati
Kode Jurnal: jptinformatikadd170135
