Performance Assessment of University Lecturers: A Data Mining Approach

Milkhatun Milkhatun(1*), Alfi Ari Fakhrur Rizal(2), Ni Wayan Wiwin Asthiningsih(3), Asslia Johar Latipah(4),

(1) Department of Nurse Universitas Muhammadiyah Kalimantan Timur
(2) Department of Nurse Universitas Muhammadiyah Kalimantan Timur
(3) Department of Nurse Universitas Muhammadiyah Kalimantan Timur
(4) Department of Informatics Universitas Muhammadiyah Kalimantan Timur
(*) Corresponding Author


A lecturer with a good performance has a positive impact on the quality of teaching and learning. The said quality  includes the delivery of teaching materials, learning methods, and ultimately the academic results of students. Performance of lecturers contributes significantly to the quality of research and community service which in turn improves the quality of teaching materials. It is desirable, therefore, to have a method to measure the performance of lecturers in carrying out the Tri Dharma (or the three responsibility) activities, which consist of teaching and learning process, research, and community service activities, including publications at both national and international level. This study seeks to measure the performance of lecturers and cluster them into three categories, namely "satisfactory", "good", and "poor". Data were taken from academic works of nursing study program lecturers in conducting academic activities. Clustering process is carried out using two machine learning approaches, which is K-Means and K-Medoids algorithms. Evaluation of the clustering results suggests that K-Medoids algorithm performs better compared to using K-Means. DBI score for clustering techniques using K-Means is -0.417 while the score for K-Medoids is -0.652. The significant difference in the score shows that K-Medoids algorithm works better in determining the performance of lecturers in carrying out Tri Dharma activities.


Data mining; K-Medoids algorithm; Lecturer performance

Full Text:



J. Abu-Doleh, and D. Weir, "Dimensions of performance appraisal systems in Jordanian private and public organizations," International Journal of Human Resource Management, 18(1), 75-84, 2007

P. M. Muchinsky, Psychology Applied to Work (10th ed.), Summerfield, NC: Hypergraphic Press, 2012

T. T. Bi Dan, S. W. Sihwi & R. Anggrainingsih, "Implementasi Iterative Dichotomiser 3 Pada Kasus Data Kelulusan Mahasiswa S1 Di Universitas Sebelas Maret," Jurnal ITSMART Vol. No. 2 ISSN : 2301-7201, 84-91, 2015

Horison & Faisal, "Undang-undang Republik Indonesia No 14 Tahun 2005 tentang Guru dan Dosen." di (30 April 2017) DOI: 10.14710/jtsiskom.5.2.2017.89-93 , JTSiskom, e-ISSN:2338-0403, 2017

Lubna, "Akurasi Dan Akuntabilitas Penilaian Kinerja Guru Pendidikan Agama Islam, Jurnal Studi Keislaman", vol. 18. no. 1, 2014

E. Turban, J.E. Aronson and T.P. Liang, Decision Support System and Intelligent Systems – 7 th ed. Pearson Education, Inc. Pearson Education, Inc, 2005

J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2000

N. Delavari, Alaa M. El-Halees, Dr. M. Reza Beikzadeh, "Application of Enhanced Analysis Model for Data Mining Processes in Higher Educational System," In Proceedings of 6th International Conference ITHET 2005 IEEE, 2005

Al-Twijri., & A.Y. Noaman, "A New Data Mining Model Adopted for Higher Institutions." International Conference on Communication, Management and Information Technology (ICCMIT 2015) Procedia Computer Science 65 ( 2015 ) 836 – 844, 2015.

C. Romero, & S. Ventura, Educational data mining: A review of the state of the art," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 40,no. 6, pp. 601–618, Nov. 2010

M. Chalaris, S. Gritzalis, M. Maragoudakis, M. Sgouropoulou & A. Tsolakidis, "Improving Quality of Educational Processes Providing New Knowledge using Data Mining Techniques," Procedia - Social and Behavioral Sciences 147 ( 2014 ), 390 – 39, 2014

M. Goyal, and R. Vohra, "Applications of Data Mining in Higher Education," IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 2, No 1, March 2012

Y. Zhang, S. Oussena,T. Clark & H. Kim, "Use data mining to improve student retention in higher education. A case study," In Proceedings of ICEIS, 2010

X. Zhu & I. Davidson, Knowledge Discovery and Data Mining: Challenges and Realities, ISBN 9781 - 59904 - 252, Hershey, New York, 2007

B. K. Baradwaj & S. Pal, "Mining Educational Data to Analyze Students’ Performance," (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, 2011

R. S. J. Baker & K. Yacef, "The State of Educational Data Mining in 2009: A Review and Future Visions," Journal of Educational Data Mining1 (1): 3 – 17, 2009.

M. A. Tair and A. M. El-Halees, "Mining Educational Data to Improve Students’ Performance," International Journal of Information and Communication Technology Research, 2012

E. Prasetyo, Data Mining Mengolah Data Menjadi Informasi Menggunakan Matlab, Yogyakarta,Penerbit ANDI, 2014

B. Venkateswarlu, & P. G. Raju, "Mine Blood Donors Information through Improved K-Means Clustering," arXiv preprint arXiv:1309.2597, 2013

E. Sugiharti & M. A. Muslim, "On-line Clustering of Lecturers Performance of Computer Science Department of Semarang State University Using K-Means Algorithm," Journal of Theoretical and Applied Information Technology, 83(1), 2016.

J. Han, M. Kamber, and J. Pei, Data Mining Concepts and Technique Third Edition. 2012

Wikipedia, rapidminer, diakses tanggal 24 november 2019, dari

Irhamni F, Damayanti F, Khusnul K B & A Mifftachul. Optimalisasi Pengelompokan Kecamatan Berdasarkan Indikator Pendidikan Menggunakan Metode Clustering Dan Davies Bouldin Index. Seminar Nasional Sains dan Teknologi 2014. Jakarta. 2014 : 1-5.

Pedoman Operasional Penilaian Angka Kredit Kenaikan Jabatan Akademik/ Pangkat Dosen

Article Metrics

Abstract view(s): 851 time(s)
PDF: 712 time(s)


  • There are currently no refbacks.