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Marlina Marlina
Universitas Bunda Mulia
Computer Science & IT

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Implementasi Metode K-Means Clustering pada Sistem Rekomendasi Dosen Tetap Berdasarkan Penilaian Dosen Yessica Putri Santoso; Marlina Marlina; Halim Agung
Jurnal Informatika Universitas Pamulang Vol 3, No 4 (2018): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (417.747 KB) | DOI: 10.32493/informatika.v3i4.2133

Abstract

Qualified lecturers can be one of the recommendations for lecturers to become permanent lecturers. The requirement to become a lecturer is to be able to teach, educate, and broaden the horizons of the students he teaches. One way to get qualified lecturers is to evaluate the performance of lecturers at the end of each semester, to find out whether the lecturer has a good performance or not. The use of the K-means clustering method as a system aide in determining lecturer recommendations will greatly assist the university in appointing lecturers as permanent lecturers. The way to work on the K-means clustering method is to find the centroid value in the value of each lecturer and in the process of getting a permanent lecturer candidate. In the results of the calculation of the K-means clustering method for 70 lecturers' assessment data in the system, which included a decent category there were 39 data. In this case, the success rate of the K-means clustering method was obtained at 55.71% success rate.
Co-Authors Halim Agung Yessica Putri Santoso