Klasterisasi Buku dan Peminjam Buku di Perpustakaan dengan Metode Analisis Jejaring Sosial dan Deteksi Komunitas

tedy setiadi

Abstract


Saat ini, Perpustakaan Universitas sudah menyediakan sistem informasi untuk mengelola berbagai data transaksi bisnisnya. Permasalahan yang dihadapi, data yang tersedia belum dimanfaatkan  lebih lanjut  agar memperoleh pengetahuan untuk mendukung kebijakan strategis perpustakaan. Tujuan penelitian ini menemukan pengetahuan berupa klaster  buku dan klaster peminjam buku di perpustakaan dengan pendekatan Analisis jejaring sosial dan deteksi komunitas. Metode penelitian dimulai dengan mengkoleksi data peminjaman buku, mengkonstruksi  ke model graf bipartit,  memproyeksikannya  graf bipartite menjadi graf buku dan graf peminjam buku. Kemudian melakukan eksperimen membandingkan beberapa algoritma deteksi komunitas kedua graf tersebut, dengan evaluasi menggunakan metrik modularitas, performance, coverage, density dan entropy. Langkah terakhir, mengimplementasikan algoritma terbaik untuk menentukan klaster buku dan peminjam buku. Hasil eksperimen algoritma Louvain dan algoritma Eva memiliki kinerja terbaik untuk graf buku dan graf peminjam buku. Implementasi  deteksi komunitas pada graf buku diperoleh 16 klaster buku, sedangkan pada graf peminjam buku diperoleh 21 klaster peminjam buku. Klasterisasi ini berguna bagi manajemen perpustakan dalam pengembangan koleksi  serta promosi perpustakaan kepada pemustaka.

Kata kunci—Analisis Jejaring Sosial,Deteksi Komunitas,graf bipartite, peminjaman buku, klasterisasi.


References


E. Elvy and H. Heriyanto, “Peran Perpustakaan Perguruan Tinggi Dalam Mendukung Implementasi Sustainable Development Goal 4,†Baca J. Dokumentasi Dan Inf., vol. 42, no. 1, p. 153, 2021.

I. Chaidir, D. W. Aditya, and S. Sumarna, “Rancang Bangun Sistem Informasi Perpustakaan Berbasis Web Pada Mts Al – Husna Depok,†J I M P - J. Inform. Merdeka Pasuruan, vol. 5, no. 2, pp. 1–6, 2021.

C. H. Wu, T. Z. Lee, and S. C. Kao, " Knowledge discovery applied to material acquisitions for libraries," Inf. Process. Manag., vol. 40, no. 4, pp. 709–725, 2004.

S. Counts et al., “Computational social science,†vol. 323, no. February, pp. 105–108, 2014.

M. A. Javed, M. S. Younis, S. Latif, J. Qadir, and A. Baig, “Community detection in networks: A multidisciplinary review,†J. Netw. Comput. Appl., vol. 108, pp. 87–111, 2018.

L. Xin, E. Haihong, and S. Junde, " Community detection based on readers' borrowing records," Proc. - 2013 IEEE Int. Conf. Green Comput. Commun. IEEE Internet Things IEEE Cyber, Phys. Soc. Comput. GreenCom-iThings-CPSCom 2013, pp. 1001–1005, 2013.

S. Yassine, S. Kadry, and M. A. Sicilia, " Application of community detection algorithms on learning networks. The case of Khan Academy repository," Comput. Appl. Eng. Educ., no. December 2019, 2020.

M. Erfanmanesh and E. Hosseini, " Using Social Network Analysis Method to Visualize Library & Information Science Research," J. Adv. Inf. Technol., vol. 7, no. 3, pp. 177–181, 2016.

X. Liu, J. Bollen, M. L. Nelson, and H. Van De Sompel, " Co-authorship networks in the digital library research community," Inf. Process. Manag., vol. 41, no. 6, pp. 1462–1480, 2005.

Y. H. Said, E. J. Wegman, W. K. Sharabati, and J. T. Rigsby, " Social networks of author-coauthor relationships," Comput. Stat. Data Anal., vol. 52, no. 4, pp. 2177–2184, 2008.

J. Zheng, J. Gong, R. Li, K. Hu, H. Wu, and S. Yang, " Community evolution analysis based on co-author network: a case study of academic communities of the journal of ' Annals of the Association of American Geographers,'" Scientometrics, vol. 113, no. 2, pp. 845–865, 2017.

J. Choi, S. Yi, and K. C. Lee, " Analysis of keyword networks in MIS research and implications for predicting knowledge evolution," Inf. Manag., vol. 48, no. 8, pp. 371–381, 2011.

F. Tang and W. Ding, " Community detection with structural and attribute similarities," J. Stat. Comput. Simul., vol. 89, no. 4, pp. 668–685, 2019.

V. D. Blondel, J. Guillaume, and E. Lefebvre, “Fast unfolding of communities in large networks,†J. Stat. Mech. theory Exp., pp. 1–12, 2008.

M. Rosvall and C. T. Bergstrom, " Maps of random walks on complex networks reveal community structure," Proc. Natl. Acad. Sci. U. S. A., vol. 105, no. 4, pp. 1118–1123, 2008.

U. N. Raghavan, R. Albert, and S. Kumara, " Near linear time algorithm to detect community structures in large-scale networks," Phys. Rev. E - Stat. Nonlinear, Soft Matter Phys., vol. 76, no. 3, pp. 1–11, 2007.

P. Pons and M. Latapy, " Computing communities in large networks using random walks," J. Graph Algorithms Appl., vol. 10, no. 2, pp. 191–218, 2006.

T. A. Dang and E. Viennet, " Community Detection based on Structural and Attribute Similarities," Int. Conf. Digit. Soc., no. c, pp. 7–14, 2012.

S. Citraro and G. Rossetti, " Eva: Attribute-Aware Network Segmentation," vol. 1, no. Cd, pp. 1–8, 2019.

S. E. Schaeffer, " Graph clustering by flow simulation," Comput. Sci. Rev., vol. 1, no. september 1969, pp. 27–64, 2007.

H. Gmati, A. Mouakher, and I. Hilali-Jaghdam, " BI-COMDET: Community detection in bipartite networks," Procedia Comput. Sci., vol. 159, pp. 313–322, 2019.

M. E. J. Newman and M. Girvan, " Finding and evaluating community structure in networks," Phys. Rev. E - Stat. Nonlinear, Soft Matter Phys., vol. 69, no. 2 2, pp. 1–15, 2004.




DOI: https://doi.org/10.35314/isi.v7i2.2780

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