Sistem Rekomendasi Buku untuk Perpustakaan Perguruan Tinggi Berbasis Association Rule

  • Laras Dewi Adistia Universitas Gunadarma, STMIK Jakarta STI&K
  • Tubagus Mohammad Akhriza STMIK Pradnya Paramita Malang
  • Singgih Jatmiko Universitas Gunadarma
Keywords: Apriori, Association Rule, Data Mining, Library Service, Recommendation System


One of the services in the university library is an information system to find the availability of library collections and the location of each collection shelf. But not many of these systems provide a mechanism that can recommend visitors not only about the books they want, but also other related books that may be needed. This study uses association rule mining techniques that are applied to library transaction data to identify relationships between books (titles) that attract visitors' attention. Relationships are built on interesting measurements between the titles, namely support and confidence, where support determines the combination of the most frequently borrowed book titles, while confidence produces the possibility that the title of the book will be borrowed along with other books. The pattern of book titles association with high confidence indicates that the titles are very related so it is recommended for visitors to consider borrowing along with the book they are looking for. In addition, the system can also recommend the procurement of new books and rack configurations to improve the visitor's experience when searching for books on the site. In the experiment, the precision of recommendations generated from the system reached 70%. Web applications were developed to help understand the effectiveness of the recommendation system based on association rules.


Download data is not yet available.


[1] Alharthi, H., Inkpen, D., and Szpakowicz, S., 2017, A Survey of Book Recommender Systems, J. Intell. Inf. Syst., 51(1), pp. 139–160.
[2] Simović, A., 2018, A Big Data Smart Library Recommender System for an Educational Institution, Libr. Hi Tech, 36(3), pp. 498–523.
[3] Yi, K., Chen, T., and Cong, G., 2018, Library Personalized Recommendation Service Method Based on Improved Association Rules, Libr. Hi Tech, 36(3), pp. 443–457.
[4] Mansouri, A., and Soleymani Asl, N., 2019, Assessing Mobile Application Components in Providing Library Services, Electron. Libr., 37(1), pp. 49–66.
[5] Wei, Q., and Yang, Y., 2017, WeChat Library: A New Mode of Mobile Library Service, Electron. Libr., 35(1), pp. 198–208.
[6] Wang, X., Yang, M., Li, J., and Wang, N., 2018, Factors of Mobile Library User Behavioral Intention from the Perspective of Information Ecology, Electron. Libr., 36(4), pp. 705–720.
[7] Isinkaye, F. O., 2015, Recommendation Systems : Principles , Methods and Evaluation, Egypt. Informatics J., 16, pp. 261–273.
[8] Vaz, P.C., Martins de Matos, D., & Martins, B., 2012, Improving a Hybrid Literary Book Recommendation System through Author Ranking, 12th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 387–388.
[9] Vaz, P.C., Ribeiro, R., & Martins de Matos, D., 2013, Understanding Temporal Dynamics of Ratings in the Book Recommendation Scenario, 2013 International Conference on Information Systems and Design of Communication, pp. 11–15.
[10] Sarwar, B., Karypis, G., Konstan, J., and Riedl, J., 2001, Item-Based Collaborative Filtering Recommendation Algorithms, Proceedings of the 10th …, pp. 285–295.
[11] Amatriain, X., Jaimes, A., Oliver, N., and Pujol, J. M., 2011, Data Mining Methods for Recommender Systems.
[12] Mooney, R. J., and Roy, L., 2000, Content-Based Book Recommending Using Learning for Text Categorization, pp. 195–204.
[13] Wang, D., Liang, Y., Xu, D., Feng, X., and Guan, R., 2018, Knowledge-Based Systems A Content-Based Recommender System for Computer Science Publications, Knowledge-Based Syst., 157(May), pp. 1–9.
[14] Mathew, P., Kuriakose, B., and Hegde, V., 2016, Book Recommendation System through Content Based and Collaborative Filtering Method, Proceedings of 2016 International Conference on Data Mining and Advanced Computing, SAPIENCE 2016.
[15] Vaz, P.C., Ribeiro, R., & Martins de Matos, D., 2013, No Title, 2013 International Conference on Information Systems and Design of Communication, pp. 11–15.
[16] Raut, B. S., Thakare, V. M., and Sherekar, S. S., 2017, Book Recommendation System, Int. Innov. Res. Sci. Technol.
[17] Jomsri, P., 2014, Book Recommendation System for Digital Library Based on User Profiles by Using Association Rule, 4th International Conference on Innovative Computing Technology, INTECH 2014 and 3rd International Conference on Future Generation Communication Technologies, FGCT 2014.
[18] Rajpurkar, S., Bhatt, D., & Malhotra, P., 2015, Book Recommendation System, IJIRST–International J. Innov. Res. Sci. Technol., 1(11), pp. 314–316.
[19] Agrawal, R., and Srikant, R., 1994, Fast Algorithms for Mining Association Rules, he 20th Int. Conf. Very Large Data Bases.
[20] Agrawal, R., Imieliński, T., and Swami, A., 1993, Mining Association Rules between Sets of Items in Large Databases, ACM SIGMOD Rec.
[21] Borgelt, C., 2019, Christian Borgelt’s Web Pages, Apriori - Assoc. Rule Induction / Freq. Item Set Min., p.
[22] Borgelt, C., 2017, Find Frequent Item Sets and Association Rules with the Apriori Algorithm, p.
[23] Borgelt, C., 2012, Frequent Item Set Mining, Wiley Interdiscip. Rev. Data Min. Knowl. Discov.
[24] Powers, D. M. W., 2011, Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation, J. Mach. Learn. Technol.
[25] Akhriza, T. M., Ma, Y., and Li, J., 2017, Revealing the Gap Between Skills of Students and the Evolving Skills Required by the Industry of Information and Communication Technology, Int. J. Softw. Eng. Knowl. Eng., 27(05), pp. 675–698.
[26] Ju, C., Bao, F., Xu, C., and Fu, X., 2015, A Novel Method of Interestingness Measures for Association Rules Mining Based on Profit, Discret. Dyn. Nat. Soc.
Artikel Rekayasa Sistem Informasi