IMPLEMENTATION OF PARTICLE SWARM OPTIMIZATION BASED MACHINE LEARNING ALGORITHM FOR STUDENT PERFORMANCE PREDICTION
Education plays an important role in the development of a country, especially educational institutions as places where the educational process has an important goal to create quality education in improving student performance. Based on research conducted in the last few decades the quality of education in Portugal has improved, but statistics show that the failure rate of students in Portugal is high, especially in the fields of Mathematics and Portuguese. On the other hand, machine learning which is part of Artificial Intelligence is considered to be helpful in the field of education, one of which is in predicting student performance. However, measuring student performance becomes a challenge since student performance has several factors, one of which is the relationship of variables and factors for predicting the performance of participating in an orderly manner. This study aims to find out how the application of machine learning algorithms based on particle sworm optimization to predict student performance. By using experimental research methods and the results of empirical studies shown in each model, namely random forest, decision tree, support vector machine and particle swarm optimization based neural network can improve the accuracy of student performance predictions.
R. H. Pambudi, B. D. Setiawan, and Indriati, “Penerapan Algoritma C4.5 Dalam Program Untuk Memprediksi Kinerja Siswa Sekolah Menengah,” Look. Forward, Look. Back Draw. Past To Shape Futur. Mark., 2018.
H. Hamsa, S. Indiradevi, and J. J. Kizhakkethottam, “Student Academic Performance Prediction Model Using Decision Tree and Fuzzy Genetic Algorithm,” Procedia Technol., vol. 25, pp. 326–332, 2016.
U. P. Indonesia, “Peningkatan Kinerja Siswa Melaluipendekatan Saintifik Pada Pembelajaran Ipa Terpadumodel Webbed,” J. Penelit. Pendidik., vol. 17, no. 1, 2017.
P. Cortez and A. Silva, “Using data mining to predict secondary school student performance,” in 15th European Concurrent Engineering Conference 2008, ECEC 2008 - 5th Future Business Technology Conference, FUBUTEC 2008, 2008.
B. Guo, R. Zhang, G. Xu, C. Shi, and L. Yang, “Predicting Students Performance in Educational Data Mining,” in Proceedings - 2015 International Symposium on Educational Technology, ISET 2015, 2016.
T. Ginting and Y. E. Rohmadi, “Machine Learning untuk Localization Berbasis RSS Menggunakan CELL-ID GSM,” Teknomatika, vol. 7, no. 2, p. 79, 2015.
A. U. Khasanah and Harwati, “A Comparative Study to Predict Student’s Performance Using Educational Data Mining Techniques,” in IOP Conference Series: Materials Science and Engineering, 2017.
M. Pandey and S. Taruna, “Towards the integration of multiple classifiers pertaining to the Student’s performance prediction,” Perspect. Sci., vol. 8, pp. 364–366, 2016.
G. Kostopoulos, A. D. Lipitakis, S. Kotsiantis, and G. Gravvanis, “Predicting student performance in distance higher education using active learning,” in Communications in Computer and Information Science, 2017.
F. Yang and F. W. B. Li, “Study on student performance estimation, student progress analysis, and student potential prediction based on data mining,” Comput. Educ., vol. 123, no. October 2017, pp. 97–108, 2018.
A. Rachman and S. Wasiyanti, “Pengukuran Kualitas E-Commerce Shopee Terhadap Kepuasan Pengguna,” Paradig. J. Komput. dan Inform. Univ. Bina Sarana Inform., vol. 21, no. 2, pp. 143–148, 2019.
S. Wiyono and T. Abidin, “IMPLEMENTATION OF K-NEAREST NEIGHBOUR (KNN) ALGORITHM TO PREDICT STUDENT’S PERFORMANCE,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., 2018.
W. F. W. Yaacob, S. A. M. Nasir, W. F. W. Yaacob, and N. M. Sobri, “Supervised data mining approach for predicting student performance,” Indones. J. Electr. Eng. Comput. Sci., 2019.
M. Mohammadi, M. Dawodi, W. Tomohisa, and N. Ahmadi, “Comparative study of supervised learning algorithms for student performance prediction,” in 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 2019, pp. 124–127.
E. P. Rohmawan, “Menggunakan Metode Desicion Tree,” J. Ilm. MATRIK Vol.20 No.1, April 201821-30, pp. 21–30, 2018.
N. V. Krishna Rao, N. Mangathayaru, and M. Sreenivasa Rao, “Evolution and prediction of a radical multi-dimensional e-learning system with cluster-based data mining techniques,” Proc. - Int. Conf. Trends Electron. Informatics, ICEI 2017, vol. 2018-Janua, pp. 701–707, 2018.
A. Rakhman, “Prediksi Ketepatan Kelulusan Mahasiswa Menggunakan Metode Decision Tree Berbasis Particle Swarm Optimation (PSO),” Smart Comp Jurnalnya Orang Pint. Komput., vol. 6, no. 1, pp. 193–197, 2017.
Z. Ibrahim and D. Rusli, “Predicting Students’ Academic Performance: Comparing Artificial Neural Network, Decision tree, And Linear Regression,” Proc. 21st Annu. SAS Malaysia Forum, 2007.
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