CLUSTERING BIDANG KEAHLIAN MAHASISWA PADA UIN GUSDUR PEKALONGAN DENGAN ALGORITMA K-MEANS
Abstract
Assigning students to their area of expertise, appropriate calculation methods are needed so that good results can be achieved. When dividing the field of expertise, many students will find it difficult to determine the area of expertise to be taken. Therefore, recommendations are needed for them, although of course it is not easy to recommend so many students because of the large amount of data that has very many fields and records. In this study, clustering of student expertise in majors at the State Islamic University K.H. Abdurrahman Wahid Pekalongan with the k-means algorithm. The results of the clustering process show that for the numerical measure manhattan distance using the KPI majors dataset gets the best Davies Bouldin value, while the MD department dataset for the Chebychev distance numerical measure shows the best Davies Bouldin value. Overall, all data from the KPI and MD majors can be grouped properly using the k-means algorithm.
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DOI: http://dx.doi.org/10.20527/klik.v10i2.544
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