Clustering Penilaian Kinerja Dosen Menggunakan Algoritma K-Means (Studi Kasus: Universitas Dehasen Bengkulu)

Devi Sartika, Juju Jumadi

Abstract


This research was conducted to classify lecturer's performance by utilizing data mining technique. This study also aims to provide ease of information and evaluation to the lecturers and decision makers in making decisions. The application of the method used in this research is the K-Means Clustering algorithm. Several stages performed in analyzing and classifying lecturer's performance begins with the determination of the centroid value of the center at random. The K-Means algorithm process ends if there is no change of centroid value between one iteration with another iteration. The test is done by using RapidMiner Studio 7.5 application with 60 lecturer's data as input. The results of the test can be seen that the group of excellent lecturer performance amounted to 12 members with a total value of the highest 48,550 centroid, good lecturer group performance amounted to 29 members with a total value of 40,340 centroid, lecturer performance good enough group amounted to 10 members with total centroid value 37,963, poor lecturer performance amounted to 9 members with the lowest centroid value 37,033.

Keywords: Data Mining, Clustering, K-Means, Performance Lecturer


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References


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