Learning Level Prediction System Based on Student Academic Ability By Looking at the Highest Similarity Value
Abstract
There is a diversity of academic abilities of students in each Indonesian educational institution, each student has different learning abilities according to the level of learning. Educators in providing learning level predictions are done manually, so it takes a long time in predicting student learning levels. In this study, it was able to predict students' academic abilities in an easy and fast way. The method used in predicting learning levels is Case Based Reasoning. This method is able to predict the student's learning level to be (1) Very Bad, (2) Bad, (3) Medium, (4) Good, and (5) Very Good. This level of learning will be used as a benchmark for educators to provide appropriate values to students. The results of this study for the academic performance of the very poor category are 0 students, the bad category is 3 students, the medium category is 79 students, the good category is 16 students and the excellent category is 12 students. The accuracy of academic performance recommendations using confusion matrix is 91.82%.
References
O. A. Echegaray calderon and D. Barrios aranibar, “Optimal selection of factors using Genetic Algorithms and Neural Networks for the prediction of students academic performance,” Lat. Am. Congr. Comput. Intell., pp. 40–46, 2015, doi: 10.1109/LA-CCI.2015.7435976.
M. Paliwal and U. A. Kumar, “A study of academic performance of business school graduates using neural network and statistical techniques,” Expert Syst. Appl., vol. 36, no. 4, pp. 7865–7872, 2009, doi: 10.1016/j.eswa.2008.11.003.
A. Flores, L. Alfaro, and J. Herrera, “Proposal model for e-learning based on Case Based Reasoning and Reinforcement Learning,” in EDUNINE 2019 - 3rd IEEE World Engineering Education Conference: Modern Educational Paradigms for Computer and Engineering Career, Proceedings, 2019, pp. 1–6, doi: 10.1109/EDUNINE.2019.8875800.
A. Rahman, R. A. Mutiarawan, A. Darmawan, Y. Rianto, and M. Syafrullah, “Prediction of students academic success using case based reasoning,” in International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2019, pp. 171–176, doi: 10.23919/EECSI48112.2019.8977104.
A. Rahman and A. Qosim, “SISTEM CERDAS PENGELOMPOKAN MAHASISWA BERDASARKAN PREDIKSI PERFORMA BELAJAR DENGAN METODE CASE BASED REASONING,” J. Edik Inform. J. Edik Inform., vol. 8, no. 1, pp. 13–25, 2021, doi: http://dx.doi.org/10.22202/ei.2021.v8i1.5030.
F. Tempola, A. Arief, and M. Muhammad, “Combination Of Case-Based Reasoning And Nearest Neighbour For Recommendation of Volcano Status,” Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng., pp. 348–352, 2017, doi: 10.1109/ICITISEE.2017.8285525.
A. Rahman and U. Budiyanto, “Case based reasoning adaptive e-learning system based on visual-auditory-kinesthetic learning styles,” in International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2019, pp. 177–182, doi: 10.23919/EECSI48112.2019.8976921.
Abdul Rahman, Destiarini, and J. Kuswanto, “Fuzzy Logic Recommended Student Learning Levels,” J. Inform. Polinema, vol. 7, no. 2, 2021, doi: 10.33795/jip.v7i2.531.
Copyright (c) 2022 Abdul Rahman (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
