Lilis Syarifah, Imas Sukaesih Sitanggang, Pudji Muljono


The thesis is student study report which is accomplished as a requirement of graduation for Master program. Selecting study’s topic and advisors influence implementation of the study. Therefore, study’s topic is able to improve academic institution quality, however a large number of thesis documents on the repository cause difficulty to get information related to advisor’s expertness and the frequent or rare topic is former studied. Association rule mining can be used to mine information on the related item. This study aims to analyze advising patterns system in Master program on Agriculture based on supervisors and their topic research on metadata thesis of IPB repository and text documents of summary using data mining approach. The datas were collected from the repository of Bogor Agricultural University website and processed using R language programming. Pattern result of the reseach were that the most popular association on supervisor was occurred at support value of 0.00793 or equivalent to 7 theses and four popular topics were Botanical insecticide, Global warming, Upland Rice, and Land Use Change. The analysis result could be useful information to be reference or suggest future research or appropriate supervisor among agricultural.


Apriori Algorithm, Association Rule Mining, Bogor Agricultural university, Text Mining

Full Text:



Angeline, D.M.D. (2013). Association Rule Generation for Student Performace Analysis using Apriori Algorithm. The SIJ Transactions on Computer Science Engineering & its Applications. 1(1): India.

Bhujade, V., & Janwe, N.J. (2011). Knowledge Discovery in Text Mining Technique Using Association Rules Extraction. International Conference on Computational Intelligence and Communication Systems.

Erman, L.M., & Sitanggang, I.S. (2016). Clustering Undergraduate Computer Science Student Thesis Based on Frequent Itemset. I. J. Information Technology and Computer Science. 8(11), 1-7. doi: 10.5815/ijitcs.2016.11.01

Han, J., Kamber, M., & Pei, J. (2012). Data Mining : concepts and techniques. Third Edition. Elsevier.

Institut Pertanian Bogor. (2016). Visi, Misi, dan Kebijakan Mutu. Retrieved from

Khan, I.A., Woo, J., Seo, J.H., & Choi, J.T. (2015). Text Mining: Extraction of Interesting Association Rule with Frequent Itemsets Mining for Korean Languang from Unstructured Data. International Journal of Multimedia and Ubiquitous Engineerig. 11:11-20.

Kulkarni, M., & Kulkarni, S. (2016). Knowledge Discovery in Text Mining using Association Rule Extraction. International Journal of Computer Applications. 143(12).

Mhunpiew, N. (2013). A Supervisor’s Roles for Successful Thesis and Dissertation. US-China Education Review A. 3(2), 119-122.

Novitasari, W., Hermawan, A., & Abdullah, Z. (2015). A Method of Discovering Association Rules from Student Admission Dataset. International Journal of Software Engineering and Its Applications. 9(8): 51-66. doi: 10.14257/ijaseia.2015.9.8.05

Perpustakaan Pusat Institut Pertanian Bogor. (2015). MT-Agriculture. Retrieved from

Symonds Q. (2016). Bogor Agriculture University. Retrieved from

Setiawan, A. (2016). Clustering Dokumen Ringkasan Tesis Mahasiswa Pascasarjana IPB Berbasis Frequent Itemsets Menggunakan Algoritme Bisecting K-Means. IPB (Bogor Agricultural University). (in Bahasa)

Shatnawi, R., Althebyan, Q., Ghalib, B., & Al-Maolegi, M. (2014). Building a Smart Academic Advising System Using Association Rule Mining. arXiv preprint arXiv:1407.1807.




  • There are currently no refbacks.

Copyright (c) 2018 Edulib

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

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