Development of a Decision Support System for Clustering Scientific Publications Using K-Means

Fauzan Bima Putra Kencana, Rosihan Ari Yuana, Nugroho Agung Pambudi

Abstract


To improve the quality of departments within a university, a decision support system (DSS) that maps the number of scientific publications published is needed urgently today. This is because clusters and ranks for a university are determined by these scientific publications. In DSS, there is a crawler bot to recap publication data from Google Scholar and Scopus, as well as the K-means algorithm for clustering departments. DSS will produce 2 clusters with a silhouette score of 0.6245660527900866. A positive value indicates that the data in one cluster are similar. The sample used data from faculty members at the Faculty of Teacher Training and Education at Sebelas Maret University in Indonesia. The results showed that cluster one consisted of 18 departments. The average results were Scopus publication of 0.4669, Google scholar publications of 1.8955, Scopus citations of 0.8328, Google scholar citations of 18.3519. Cluster two consists of six departments., The average results were Scopus publication of 3.1727, Google Scholar publication of 4.2636, Scopus Citation of 9.0636, Google Scholar Citation of 67.6455.

Keywords


decision support system, bot crawler, K-means, google scholar, Scopus

Full Text:

PDF

References


M. F. Soni Akhmad Nulhaqim, R. Dudy Heryadi, Ramadhan Pancasilawan, “Peranan Perguruan Tinggi Dalam Meningkatkan Kualitas Pendidikan Di Indonesia Untuk Menghadapi Asean Community 201533,” Univ. Padjadjaran, vol. 6, p. 198, 2015.

S. Russell and P. Norvig, Artificial Intelligence A Modern Approach Third Edition. 2010.

Y. Widiastuti, S. W. Sihwi, and M. E. Sulistyo, “Decision Support System for House Purchasing Using Knn ( K-Nearest Neighbor ) Method,” J. Itsmart, vol. 5, no. 1, pp. 43–49, 2016.

Wibowo, Perancangan Sistem Pendukung Keputusan. Depok, 2011.

L. Richardson, “Beautiful Soup Documentation Release 4.4.0,” Media.Readthedocs.Org, pp. 1–72, 2019.

J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the fifth Berkeley Symposium on Mathematical Statistics and Probability, 1967, vol. 1, pp. 281–296.

C. D. Nguyen and T. H. Duong, “K-means** - a fast and efficient K-means algorithms,” Int. J. Intell. Inf. Database Syst., vol. 11, no. 1, p. 27, 2018.

O. Maimon and L. Rokach, Data Mining and Knowledge Discovery Handbook. Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2005.

T. Wahyono, Fundamental Of Python For Machine Learning. Yogyakarta: Gava Media, 2018.

A. Priyanto and M. R. Ma’arif, “Implementasi Web Scrapping dan Text Mining untuk Akuisisi dan Kategorisasi Informasi dari Internet (Studi Kasus: Tutorial Hidroponik),” Indones. J. Inf. Syst., vol. 1, no. 1, pp. 25–33, 2018.

V. Singrodia, A. Mitra, and S. Paul, “A Review on Web Scrapping and its Applications,” in 2019 International Conference on Computer Communication and Informatics, ICCCI 2019, 2019.

M. Walther and B. Melsheimer, “Automated author affiliation processing using Scopus data,” Procedia Comput. Sci., vol. 146, pp. 53–59, 2019.

M. E. Rose and J. R. Kitchin, “pybliometrics: Scriptable bibliometrics using a Python interface to Scopus,” SoftwareX, vol. 10, p. 100263, 2019.

G. Varoquaux, L. Buitinck, G. Louppe, O. Grisel, F. Pedregosa, and A. Mueller, “Scikit-learn,” GetMobile Mob. Comput. Commun., vol. 19, no. 1, pp. 29–33, 2015.

A. Martín-Martín, E. Orduna-Malea, M. Thelwall, and E. Delgado López-Cózar, “Google Scholar, Web of Science, and Scopus: A systematic comparison of citations in 252 subject categories,” J. Informetr., vol. 12, no. 4, pp. 1160–1177, 2018.




DOI: http://dx.doi.org/10.52155/ijpsat.v27.2.3322

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Fauzan Bima Putra Kencana, Rosihan Ari Yuana, Nugroho Agung Pambudi

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.