PENGUKURAN KEAKTIFAN DOSEN DALAM PEMBELAJARAN BERBASIS SMARTLECTURER MENGGUNAKAN FUZZY MAMDANI DAN SUGENO

Authors

  • Muhammad Yasin Politeknik LP3I Jakarta

Abstract

The success of learning in a classroom that uses supporting media demands lecturer activity.  The level of activity of lecturers in class and using learning media such as Smartlecturer can be measured using a fuzzy logic approach. This study aims to measure the level of active lecturers using the approach, namely: Mamdani and Sugeno method. Stages of lecturer activity measurement by forming a fuzzy set, composition rules as many as 24 rules and the defuzzification process using the centroid method that produces the level of activity of each is low, medium and high. Based on 173 lecturer activity data, the results of the mamdani method low 66%, medium 31%, and high 3%. While the Sugeno method produces a level of activity low 75%, moderate 17%, and high 8%. Therefore, Mamdani method is more suitable for the calculation because the spread of results is relatively evenly distributed at each level.

 

Key words: Lecturer, Fuzzy, Activeness, Mamdani, Sugeno

References

Abdel-Aleem, A., El-Sharief, M. A., Hassan, M. A., & El-Sebaie, M. G. (2017). Implementation of fuzzy and adaptive neuro-fuzzy inference systems in optimization of production inventory problem. Appl. Math. Inf. Sci, 11(1), 289-298.

Aghakhani, S., & Dick, S. (2010, July). An on-line learning algorithm for complex fuzzy logic. In Fuzzy Systems (FUZZ), 2010 IEEE International Conference on (pp. 1-7). IEEE.

Alkandari, A. A., & Al-Shaikhli, I. F. (2018). Implementation of Dynamic Fuzzy Logic Control of Traffic Light with Accident Detection and Action System using iTraffic Simulation. Indonesian Journal of Electrical Engineering and Computer Science, 10(1), 100-109.

Angelov, P. P., & Filev, D. P. (2004). An approach to online identification of Takagi-Sugeno fuzzy models. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(1), 484-498.

Asopa, P., Asopa, S., Joshi, N., & Mathur, I. (2016, September). Evaluating student performance using fuzzy inference system in fuzzy ITS. In Advances in Computing, Communications and Informatics (ICACCI), 2016 International Conference on (pp. 1847-1851). IEEE.

Boelens, R., Voet, M., & De Wever, B. (2018). The design of blended learning in response to student diversity in higher education: Instructors’ views and use of differentiated instruction in blended learning. Computers & Education, 120, 197-212.

Cadez, S., Dimovski, V., & Zaman Groff, M. (2017). Research, teaching and performance evaluation in academia: the salience of quality. Studies in Higher Education, 42(8), 1455-1473.

Cano-Hurtado, J. J., Carot-Sierra, J. M., Fernandez-Prada, M. A., & Fargueta, F. (2011). An evaluation model of the teaching activity of academic staff. http://www.oecd.org/dataoecd/4/29/43977296.pdf

Cavanagh, M. (2011). Students’ experiences of active engagement through cooperative learning activities in lectures. Active Learning in Higher Education, 12(1), 23-33.

Cavus, N. (2010). The evaluation of Learning Management Systems using an artificial intelligence fuzzy logic algorithm. Advances in Engineering Software, 41(2), 248-254.

De Freitas, S. I., Morgan, J., & Gibson, D. (2015). Will MOOCs transform learning and teaching in higher education? Engagement and course retention in online learning provision. British Journal of Educational Technology, 46(3), 455-471.

Deborah, L. J., Sathiyaseelan, R., Audithan, S., & Vijayakumar, P. (2015). Fuzzy-logic based learning style prediction in e-learning using web interface information. Sadhana, 40(2), 379-394.

Goyal, M., & Krishnamurthy, R. (2018). Optimizing Student Engagement in Online Learning Environments: Intuitionistic Fuzzy Logic in Student Modeling. In Optimizing Student Engagement in Online Learning Environments (pp. 187-219). IGI Global

Hasan, M. H., Aziz, I. A., Jaafar, J., Rahim, L. A., & Manyiel, J. M. A. (2017). A Comparative Study of Mamdani and Sugeno Fuzzy Models for Quality of Web Services Monitoring. INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 8(9), 350-356.

Jeffrey, L. M., Milne, J., Suddaby, G., & Higgins, A. (2014). Blended learning: How teachers balance the blend of online and classroom components. Journal of Information Technology Education, 13.

Khairudin, M., Riyanto, S., & Mohammed, Z. (2018). Development of Fuzzy Logic Control for Indoor Lighting Using LEDs Group. Telkomnika, 16(3).

Kusumadewi, Sri. 2003. Artificial Intelligence (Teknik dan Aplikasinya). Graha Ilmu. Yogyakarta.

Lochner, L., Wieser, H., Waldboth, S., & Mischo-Kelling, M. (2016). Combining traditional anatomy lectures with e-learning activities: how do students perceive their learning experience?. International journal of medical education, 7, 69.

Luo, L., Cheng, X., Wang, S., Zhang, J., Zhu, W., Yang, J., & Liu, P. (2017). Blended learning with Moodle in medical statistics: an assessment of knowledge, attitudes and practices relating to e-learning. BMC medical education, 17(1), 170.

McCabe, A., & O'Connor, U. (2014). Student-centred learning: the role and responsibility of the lecturer. Teaching in Higher Education, 19(4), 350-359.

Raphael, C., & Mtebe, J. (2016). Instructor support services: An inevitable critical success factor in blended learning in higher education in Tanzania. International Journal of Education and Development using ICT, 12(2).

Singla, J. (2015, March). Comparative study of Mamdani-type and Sugeno-type fuzzy inference systems for diagnosis of diabetes. In Computer Engineering and Applications (ICACEA), 2015 International Conference on Advances in (pp. 517-522). IEEE.

Verma, S. K., Thakur, R. S., & Jaloree, S. (2017). Fuzzy association rule mining based model to predict students’ performance. International Journal of Electrical and Computer Engineering (IJECE), 7(4), 2223-2231.

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Published

2022-11-18

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JURNAL LENTERA ICT