Sains Malaysiana 48(12)(2019): 2831–2839

http://dx.doi.org/10.17576/jsm-2019-4812-24

A Relative Tolerance Relation of Rough Set in Incomplete Information

(Perhubungan Toleransi Relatif Set Kasar dalam Maklumat tak Lengkap)

RD ROHMAT SAEDUDIN¹*, SHAHREEN KASIM², HAIRULNIZAM MAHDIN², MOHD FARHAN MD FUDZEE², EDI SUTOYO¹, IWAN TRI RIYADI YANTO³, ROHAYANTI HASSAN⁴

¹School of Industrial Engineering, Telkom University, 40257, Bandung, West Java, Indonesia

²Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400 Batu Pahat, Johor Darul Takzim, Malaysia

³Department of Information Systems, Universitas Ahmad Dahlan, 55161, Yogyakarta, Indonesia

⁴Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor Darul Takzim, Malaysia

Received: 21 February 2019/Accepted: 25 December 2019

ABSTRACT

University is an educational institution that has objectives to increase student retention and also to make sure students graduate on time. Student learning performance can be predicted using data mining techniques e.g. the application of finding essential association rules on student learning base on demographic data by the university in order to achieve these objectives. However, the complete data i.e. the dataset without missing values to generate interesting rules for the detection system, is the key requirement for any mining technique. Furthermore, it is problematic to capture complete information from the nature of student data, due to high computational time to scan the datasets. To overcome these problems, this paper introduces a relative tolerance relation of rough set (RTRS). The novelty of RTRS is that, unlike previous rough set approaches that use tolerance relation, non-symmetric similarity relation, and limited tolerance relation, it is based on limited tolerance relation by taking account into consideration the relatively precision between two objects and therefore this is the first work that uses relatively precision. Moreover, this paper presents the mathematical properties of the RTRS approach and compares the performance and the existing approaches by using real-world student dataset for classifying university’s student performance. The results show that the proposed approach outperformed the existing approaches in terms of computational time and accuracy.

Keywords: Classification; educational data mining; incomplete information systems; rough set theory

ABSTRAK

Universiti adalah sebuah institusi pendidikan yang antara objektifnya adalah untuk meningkatkan penahanan pelajar dan juga untuk memastikan pelajar bergraduasi dalam jangka masa yang ditetapkan. Untuk mencapai objektif tersebut, pelajar perlulah memastikan prestasi pembelajaran sentiasa konsisten. Teknik perlombongan data boleh digunakan untuk meramal prestasi pembelajaran pelajar. Namun, isu data hilang atau data tidak lengkap membataskan keberkesanan teknik perlombongan data khasnya dalam mengenal pasti hubungan atribut pembelajaran pelajar dan atribut demografi pelajar. Isu menjadi lebih sukar apabila melibatkan data pelajar yang banyak. Maka, kertas ini mencadangkan teknik perhubungan toleransi relatif set kasar (RTRS) bagi mengatasi isu ini. Kelainan RTRS dalam kertas ini adalah dengan menggunakan ketepatan relatif antara dua objek atribut. Selain itu, kertas ini turut membentangkan formula matematik yang digunakan dalam RTRS. Seterusnya, prestasi cadangan teknik RTRS ini dibandingkan dengan teknik asal menggunakan set data pelajar universiti untuk mengelaskan prestasi pelajar tersebut. Hasil menunjukkan bahawa teknik RTRS yang dicadangkan mengatasi teknik sedia ada daripada segi masa komputer dan ketepatan.

Kata kunci: Pengelasan; perlombongan data pendidikan; sistem maklumat tidak lengkap; teori set kasar

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*Corresponding author; email: rdrohmat@telkomuniversity.ac.id