Expert System Development for Course Enrollment Process Using Ripple Down Rules in a University in Surabaya

Agus Cahyo Nugroho


The research aimed to help lecturers to decide which courses to be taken by students in the next semester. The researcher developed the expert system contained specific pieces of knowledge to solve specific problems involved in the forms of system development and maintenance. This expert system was in the form of a website using the PHP programming language and MySQL database. The researcher used the Ripple Down Rules (RDR) method to identify the courses by putting forward the questions so that the system could decide which courses the students should take in next semester. The result shows that this web-based expert system can identify which courses that the students have to enroll after the students have answered questions generated by the system. The available data on the courses in the system adapt to the rules, so it is in line with the enrolled courses.


expert system, course enrollment, Ripple Down Rules (RDR)

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Anam, S., Kim, Y. S., Kang, B. H., & Liu, Q. (2015). Schema mapping using hybrid ripple-down rules. In 38th Australasian Computer Science Conference (ACSC 2015) (Vol. 159, pp. 17-26).

Bratko, I. (2001). Prolog programming for artificial intelligence. Pearson Education.

Durkin, J. (1994). Expert systems: Design and development. New York: Macmillan.

Galgani, F., Compton, P., & Hoffmann, A. (2015). Lexa: Building knowledge bases for automatic legal citation classification. Expert Systems with Applications, 42(17-18), 6391-6407.

Han, S. C., Mirowski, L., Jeon, S. H., Lee, G. S., Kang, B. H., & Turner, P. (2013). Expert systems and home based telehealth: Exploring a role for MCRDR in enhancing diagnostics. In International Conference, UCMA, SIA, CCSC, ACIT 2013 (Vol. 22, pp. 121-127).

Han, S. C., Yoon, H. G., Kang, B. H., & Park, S. B. (2014). Using MCRDR based agile approach for expert system development. Computing, 96(9), 897-908.

Hussain, M., Hassan, A. U., Sadiq, M., Kang, B. H., & Lee, S. (2018). Missing information prediction in ripple down rule based clinical decision support system. In International Conference on Smart Homes and Health Telematics (pp. 179-188).

Patrzek, J., Sattler, S., van Veen, F., Grunschel, C., & Fries, S. (2015). Investigating the effect of academic procrastination on the frequency and variety of academic misconduct: A panel study. Studies in Higher Education, 40(6), 1014-1029.

Richards, D. (2009). Two decades of ripple down rules research. The Knowledge Engineering Review, 24(2), 159-184.

Shirazi, H., & Sammut, C. A. (2008). Acquiring control knowledge from examples using ripple-down rules and machine learning. Iranian Journal of Science and Technology, 32(B3), 295-304.

Zeenath, S., & Orcullo, D. J. C. (2012). Exploring academic procrastination among undergraduates. International Proceedings of Economics Development & Research, 47(9), 42-46.



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