Modeling Mathematics Performance Between Rural and Urban School Using a Fuzzy Logic Approach

Nor Azriani Mohamad Nor; Azlinda Azizan; Balkiah Moktar; Azlan Abdul Aziz; Diana Sirmayunie Mohd Nasir

doi:10.24191/jcrinn.v6i1.176

Modeling Mathematics Performance Between Rural and Urban School Using a Fuzzy Logic Approach

Authors

Nor Azriani Mohamad Nor Universiti Teknologi MARA, Perlis Branch, Arau Campus
Azlinda Azizan Universiti Teknologi MARA, Perlis Branch, Arau Campus
Balkiah Moktar Universiti Teknologi MARA, Perlis Branch, Arau Campus
Azlan Abdul Aziz Universiti Teknologi MARA, Perlis Branch, Arau Campus
Diana Sirmayunie Mohd Nasir Universiti Teknologi MARA, Perlis Branch, Arau Campus

DOI:

https://doi.org/10.24191/jcrinn.v6i1.176

Keywords:

Sijil Pelajaran Malaysia, mathematics, urban school, rural school, fuzzy logic

Abstract

This study concerns competitiveness in Sijil Pelajaran Malaysia (SPM) performance between two different schools in Kedah, Malaysia, focusing on Mathematics scores. There are two different schools selected namely SMK Sungai Layar and SMK Bandar Sungai Petani. SMK Sungai Layar is a rural school while SMK Bandar Sungai Petani is an urban school. The objectives are to determine which schools between urban and rural schools perform better in mathematics subjects and classify students' performance on Mathematics subject using Fuzzy Logic. It is found that the performance of urban school was better than the rural school. As for rural school, the performance was moderate. The percentage of Mathematics value for SMK Bandar Sungai Petani is higher than SMK Sungai Layar. The number of students from an urban school who got a good score was double from the number of students from rural schools. The results show that the students from the urban school have excellent flexibility and reliability in Mathematics subject.

Downloads

Download data is not yet available.

References

Ajiboye, A. R., Arshah, R. A., & Qin, H. (2013). Risk status prediction and modelling of students' academic achievement: A fuzzy logic approach. International Journal of Engineering and Science,3(11), 7-14.

Arora, N., & Saini, J. R. (2014). Predicting student academic performance using fuzzy ARTMAP network. International Journal of Advances in Engineering Science and Technology, 3(3), 187-192.

Habibah, A.R. (2020 March 5), SPM 2019: 8,876 peroleh keputusan cemerlang, Bernama https://www.mstar.com.my/lokal/semasa/2020/03/05/spm2019.

Hassan, O, R., & Rasiah, R. (2011). Poverty and student performance in Malaysia. International Journal of Institutions and Economies, 3(1), 61-76.

Ingoley, S. N., & Bakal, J. W. (2012). Evaluating students performance using fuzzy logic. In International Conference, IJCA Proceedings on International Conference on Recent Trends in Information Technology and Computer Science, ICRTITCS (9) (pp. 15-20).

Jamsandekar, S, S., & Mudholkar, R, R. (2013). Performance evaluation by fuzzy inference technique. International Journal of Soft Computing and Engineering, 3(2), 158-164.

Jawahar, J., & Seema, S. (2009). Cost accounting. Retrieved from https://books.google.com.my/books?id=1KklpFKeT6EC&pg=PA147&lpg=PA147&dq=average+is+simple+method+to+compare&source=bl&ots=Cd4G NZ_rHA&sig=7VRot0-

Kharola, A., Kunwar, S., & Choudhury, G. B. (2015). Students performance evaluation: A fuzzy logic reasoning approach. PM World Journal, 4(9), 1-11.

Krouska, A., Troussas, C., & Sgouropoulou, C. (2019). Fuzzy logic for refining the evaluation of learnersâ€™ performance in online engineering education. European Journal of Engineering and Technology Research, 4(6), 50-56.

Mohamed Shahiri, A., Husaina, W., & Abdul Rashid, N. (2015). A review on predicting studentâ€™s performance using data mining techniques. Procedia Computer Science, 72, 414 â€“ 422.

Nguyen, C. H., Pedrycz, W., Duong, T. L., & Tran, T. S. (2013). A genetic design of linguistic terms for fuzzy rule based classifiers. International Journal of Approximate Reasoning, 54(1), 1-21.

Sakthivel, E., Kannan, K. S., & Arumugam, S. (2013). Optimized evaluation of students performance using fuzzy logic. International Journal of Scientific & Engineering Research, 4(9), 1128-1133.

SPM results best in five years. (2014, Mac). Retrieved December 1, 2016, from http://www.barisannasional.org.my/news/spm-results-best-in-five-years

Yadav, R. S. & Vijendra, P. S.(2011). Modeling academic performance evaluation using soft computing techniques: A fuzzy logic approach. International Journal on Computer Science and Engineering (IJCSE),3(2), 676-686.

Yadav, R. S., & Ahmed, P. (2013). Modeling academic performance evaluation using subtractive clustering approach. International Journal of Computer Science and Technology, 4, 73-80.

Yadav, R. S., & Singh, V. P. (2012). Modeling academic performance evaluation using fuzzy c-means clustering techniques. International Journal of Computer Application, 60(8), 15-23.

Zimmermann, H. J. (2010). Fuzzy set theory. Wiley Interdisciplinary Review: Computational Statistics, 2(3), 317-332.