Implementation of K-Means Technique in Data Mining to Cluster Researchers Google Scholar Profile
Gigih Forda Nama1, Lukmanul Hakim2, Junaidi3
1Gigih Forda Nama*, Department of Informatics, University of Lampung.
2Lukmanul Hakim, Department of Electrical Engineering, University of Lampung.
3Junaidi, Department of Physics, University of Lampung.
Manuscript received on September 11, 2019. | Revised Manuscript received on September 22, 2019. | Manuscript published on October 30, 2019. | PP: 3654-3660 | Volume-9 Issue-1, October 2019 | Retrieval Number: A2708109119/2019©BEIESP | DOI: 10.35940/ijeat.A2708.109119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A university usually has many Lecturers that have an important role in improving the quality of Higher Education. The Lecturers should produce scientific publications at least 1 publication on each semester. The achievements of a Lecturer in research and publication become the main indicator that describes the professionalism of lecturers as scientists. Monitoring the improvement of publication trends is very important to do as an evaluation for organizational management in choosing the best strategy to strengthen the quality of publication, and one of the common tools used for analyzing the publication data is the Google Scholar system. This paper attempts to analyze the Google scholar data using Data Mining techniques (Text Mining) by R language, to collect Lecturer’s profile and list of publications in a real-time, the aim of this research is to allow the Management for identifying the Cluster from total 1039 Lecturers on University of Lampung. The results of this research shown there were 5 Cluster of scholar profile data, with member details C0=102, C1=924, C2=1, C3=1, C4=11, total 88.93% of Lecturers are on cluster C1 with, centroid data was h_index=1.942, total_cites=20.89, i10_index=0.417. X.
Keywords: Text Mining, R Language, Clustering, Data Mining, Google Scholar, Publication Analysis.