Yelfi Vitriani


One of the weaknesses of the Bayesian Network is that it is difficult to get agreement from some experts, for an expert it will be difficult to determine the probability value, and an expert will take a long time just to build the Bayesian Network structure. To overcome the weakness of the Bayesian Network then required another artificial intelligence science that is data mining with Association Rule technique using FP-Growth algorithm. This research takes the case on eye disease with the aim of building a Bayesian Network structure and generating probability values to get where the most influential symptoms in eye disease. The method test is carried out by using data mining tools WEKA 3.7.10, with the results obtained by 24 rules that meet the provisions and qualitative test results of 99% correct and get the probability value for presbyopia disease, with the greatest influence on women, evidenced by probability value of 60 %. For the most influential age was the mature middle of 31-59 years at 65%, and the most influential symptom was a near blur of 98%. As for conjunctivitis disease with the largest influence on men by 53%.For the most influential age is the middle adult from 31-59 years by 43%, and the most influential symptoms are the sticky eye of 100%. Based on the results of these tests can be concluded that the Association Rule technique succeeded in overcoming Bayesian Network weaknesses based on facts and data.

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Ma, D. L., zhang, w. j., Dong, B., Yang, P., dan Lu, H. X. (2008). Establishing Knowledge Base of Expert System With Association Rules. International.

Ghosh, J. K. (1999). Probabilistic Bayesian Network MOdel Building of Heart Disease. Columbia.

Rahmad, K., Yanti, N., dan Nazri, M. Z. (2014). "Expert system for self-diagnosing of eye diseases using Naive Bayes". Advanced informatics: Concept, Theroey and application (ICAICTA), International Conference of. IEEE.

Kurniawan, R. (2011). Sistem pakar untuk mendiagnosa penyakit mata menggunakan metode Bayesian Network. Pekanbaru: Universitas Sultan Syarif Kasim Riau.

Marcot, B. G. (2001). Using Bayesian belief networks to evaluate fish and wildlife population viability under land management alternatives from environmental impact statement. Forest ecology and management . 153(1) 29-42.

Uusitalo, L. (2007). Advantages and challenges of Bayesian networks in environmental modelling. Ecological modelling,. 203(3), 312-318.

Hart, B., Pollino, C. (2008). Increased use of Bayesian Network models will improve eological risk assessments.

Kragt, M. E. (2009). A beginners guide to Bayesian Network modelling for integrated catchment management. Landscape Logic.

Samuel, D. (2008). Penerapan Stuktur FP-Tree dan Algoritma FP-Growth dalam Optimasi Penentuan Frequent Itemset. . Institut Teknologi Bandung: 1.

Pinheiro, F. K. (2013). Extracting association rules from liver cancer data using the FP-growth algorithm. In Computational Advances in Bio and Medical Sciences (ICCABS). IEEE 3rd International Conference.

Fayyad, U., Piatetsky-Shapiro, G., dan Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. American Association for Artificial Intelligence.

Han, J. (2006). Data Mining: Concepts and Techniques. 2nd edition. Elsevier Inc.

Tian, D. G. (2013). A Bayesian Association Rule Mining Algorithm. In 2013 IEEE International Conference on Systems, Man, and Cybernetics (pp. 3258-3264). IEEE.

Samuel, D. (2008). Penerapan Stuktur FP-Tree dan Algoritma FP-Growth dalam Optimasi Penentuan Frequent Itemset. . Institut Teknologi Bandung: 1.

Heckerman, D. (1998). A tutorial on learning with Bayesian Networks. In Learning in graphical model. Springer Netherlands.


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