Clustering Mahasiswa Baru Menggunakan K-Means Untuk Perencanaan Teknologi Pembelajaran di Unilak
DOI:
https://doi.org/10.31849/23d8ex68Keywords:
Clustering , k-means, Data Mining, new students, education technologyAbstract
Mapping the characteristics of new students is an important first step in designing a technology-based education system, particularly at private universities, where competition to attract new students is intense. This study uses the K-means algorithm to cluster new students at Lancang Kuning University. The goal is to identify patterns that can inform the design of adaptive learning systems. The study employs a quantitative approach with data mining techniques using new student data with variables of academic and digital literacy scores. The clustering results revealed three groups: 54 students with high scores and high digital literacy, 52 students with moderate scores and moderate digital literacy, and 44 students with low scores and low digital literacy. Three main clusters emerged from the data clustering: digitally independent students, moderate students, and students requiring assistance and guidance. These results indicate differences in the learning needs of new students and support strategic recommendations for implementing educational technology tailored to their needs. These results contribute to the use of data mining technology for data-driven learning planning at Lancang Kuning University
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