Implementasi Limited Tolerance Relation Untuk Sistem Informasi yang Tidak Lengkap Pada Data Mahasiswa


  • Edi Sutoyo Program Studi Sistem Informasi, Universitas Telkom, Bandung, Jawa Barat, Indonesia, 40257



rough set, limited tolerance relation, incomplete information system, educational data mining


Rough set theory is one of the mathematical models for dealing with a vague, imprecise, and fuzzy knowledge that has been successfully used to handle incomplete information systems. Since, in fact in the real-world problems, often found the conditions that are the user can not provide all the necessary preference values. In this research, an implementation of extension technique of rough set theory that called limited tolerance relation is used to overcome incomplete information systems in student data at Telkom University. Based on the obtained results, the limited tolerance relation technique successfully used to handle that problem. The results show that the technique achieved the  accuracy of 96.04% with an execution time of 3.1830 seconds.


C. Romero and S. Ventura, “Educational data mining: A survey from 1995 to 2005,” Expert Syst. Appl., vol. 33, no. 1, pp. 135–146, 2007.

S. Kotsiantis, C. Pierrakeas, and P. Pintelas, “Predicting Students’performance In Distance Learning Using Machine Learning Techniques,” Appl. Artif. Intell., vol. 18, no. 5, pp. 411–426, 2004.

S. K. Yadav and S. Pal, “Data mining: A prediction for performance improvement of engineering students using classification,” arXiv Prepr. arXiv1203.3832, 2012.

S. Pal, “Mining educational data to reduce dropout rates of engineering students,” Int. J. Inf. Eng. Electron. Bus., vol. 4, no. 2, p. 1, 2012.

S. Borkar and K. Rajeswari, “Predicting students academic performance using education data mining,” IJCSMC Int. J. Comput. Sci. Mob. Comput. ISSN, pp. 273–279, 2013.

S. K. Yadav, B. Bharadwaj, and S. Pal, “Mining Education data to predict student’s retention: a comparative study,” arXiv Prepr. arXiv1203.2987, 2012.

B. Minaei-Bidgoli, D. A. Kashy, G. Kortemeyer, and W. F. Punch, “Predicting student performance: an application of data mining methods with an educational web-based system,” in Frontiers in education, 2003. FIE 2003 33rd annual, 2003, vol. 1, p. T2A--13.

B. P. Bunting, G. Adamson, and P. K. Mulhall, “A Monte Carlo examination of an MTMM model with planned incomplete data structures,” Struct. Equ. Model., vol. 9, no. 3, pp. 369–389, 2002.

M. R. Chmielewski, J. W. Grzymala-Busse, N. W. Peterson, and S. Than, “The rule induction system LERS-a version for personal computers,” Found. Comput. Decis. Sci., vol. 18, no. 3–4, pp. 181–212, 1993.

Z. Pawlak, “Rough sets,” Int. J. Comput. Inf. Sci., vol. 11, no. 5, pp. 341–356, 1982.

D. Molodtsov, “Soft set theory—first results,” Comput. Math. with Appl., vol. 37, no. 4–5, pp. 19–31, 1999.

E. Sutoyo, I. T. R. Yanto, R. R. Saedudin, and T. Herawan, “A soft set-based co-occurrence for clustering web user transactions,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 15, no. 3, 2017.

I. T. R. Yanto, M. A. Ismail, and T. Herawan, “A modified Fuzzy k-Partition based on indiscernibility relation for categorical data clustering,” Eng. Appl. Artif. Intell., vol. 53, pp. 41–52, 2016.

Z. Pawlak, “Some remarks on conflict analysis,” Eur. J. Oper. Res., vol. 166, no. 3, pp. 649–654, 2005.

E. Sutoyo, M. Mungad, S. Hamid, and T. Herawan, “An efficient soft set-based approach for conflict analysis,” PLoS One, vol. 11, no. 2, 2016.

Z. Kong, L. Gao, L. Wang, and S. Li, “The normal parameter reduction of soft sets and its algorithm,” Comput. Math. with Appl., vol. 56, no. 12, pp. 3029–3037, 2008.

M. A. T. Mohammed, W. M. W. Mohd, R. A. Arshah, M. Mungad, E. Sutoyo, and H. Chiroma, “ANALYSIS OF PARAMETERIZATION VALUE REDUCTION OF SOFT SETS AND ITS ALGORITHM,” Int. J. Softw. Eng. Comput. Syst., vol. 2, no. 1, pp. 51–57, 2016.

G. Wang, “Extension of rough set under incomplete information systems,” in Fuzzy Systems, 2002. FUZZ-IEEE’02. Proceedings of the 2002 IEEE International Conference on, 2002, vol. 2, pp. 1098–1103.

M. Kryszkiewicz, “Rough set approach to incomplete information systems,” Inf. Sci. (Ny)., vol. 112, no. 1, pp. 39–49, 1998.

M. Kryszkiewicz, “Rules in incomplete information systems,” Inf. Sci. (Ny)., vol. 113, no. 3, pp. 271–292, 1999.

J. Zhou and X. Yang, “Rough set model based on hybrid tolerance relation,” in International Conference on Rough Sets and Knowledge Technology, 2012, pp. 28–33.

J. Stefanowski and A. Tsoukiàs, “On the extension of rough sets under incomplete information,” in International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, 1999, pp. 73–81.