Improved SVM Classification Using Particle Swarm Optimization for Student Completion Prediction System
DOI:
https://doi.org/10.30595/juita.v12i2.21990Keywords:
PSO, SVM, optimization, student completion prediction, data miningAbstract
Timely completion of a study program is crucial for evaluating the quality of universities. To achieve timely completion, student’s progress needs to be monitored early in order to ensure that they can complete the given task on time. This process is particularly important because universities often enroll thousands of students, thereby making individual supervision impractical. An effective solution to this problem is leveraging machine learning to develop a system that predicts whether student will complete the study without delay. Therefore, this study used Support Vector Machine (SVM) method for classification, with RBF kernel. Optimization of SVM classification was achieved by ensuring the values for Soft Margin C parameter and kernel parameter were correct. In addition, Particle Swarm Optimization (PSO) method was used to determine the optimal SVM parameter values. Consequently, the resulting model was evaluated using Cross Fold Validation. The optimized SVM parameter identified through PSO were gamma of 0.0085 and C of 0.4196. The average training accuracy recorded is 82.58%, with 81.22% validation, these results can be categorized into Good Classification. Finally, the application of PSO in optimization resulted in SVM models that avoided overfitting, as shown by the closeness of training and validation values.
References
[1] O. Maimon and L. Rokach, Data mining and knowledge discovery handbook, vol. 48, no. 10. 2011.
[2] M. S. Zulfiker, N. Kabir, A. A. Biswas, P. Chakraborty, and M. M. Rahman, “Predicting students’ performance of the private universities of Bangladesh using machine learning approaches,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 3, pp. 672–679, 2020, doi: 10.14569/ijacsa.2020.0110383.
[3] I. Romli and A. T. Zy, “Penentuan Jadwal Overtime Dengan Klasifikasi Data Karyawan Menggunakan Algoritma C4.5,” J. Sains Komput. Inform. (J-SAKTI, vol. 4, no. 2, pp. 694–702, 2020.
[4] N. Priyasadie and S. M. Isa, “Educational Data Mining in Predicting Student Final Grades on Standardized Indonesia Data Pokok Pendidikan Data Set,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 12, pp. 212–216, 2021, doi: 10.14569/IJACSA.2021.0121227.
[5] M. Y. Cho and T. T. Hoang, “Feature Selection and Parameters Optimization of SVM Using Particle Swarm Optimization for Fault Classification in Power Distribution Systems,” Comput. Intell. Neurosci., vol. 2017, 2017, doi: 10.1155/2017/4135465.
[6] X. Yao, “Application of Optimized SVM in Sample Classification,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 6, pp. 540–547, 2022, doi: 10.14569/IJACSA.2022.0130666.
[7] I. Hidayat, “Prediksi kelulusan tepat waktu menggunakan algoritma svm dan k-nearest neighbour berbasis particle swarm optimization di STIMK Eresha,” J. ESIT (E-Bisnis, Sist. Inf. , Teknol. Informasi), vol. 16, no. 01, pp. 55–65, 2021.
[8] Z. Zhao, Yuying Song, F. Cui, J. Zhu, Z. Xu., “Point Cloud Features-Based Kernel SVM for Human-Vehicle Classification in Millimeter Wave Radar,” IEEE Access, vol. 8, pp. 26012–26021, 2020, doi: 10.1109/ACCESS.2020.2970533.
[9] G. Lantzanakis, Z. Mitraka, and N. Chrysoulakis, “X-SVM: An Extension of C-SVM Algorithm for Classification of High-Resolution Satellite Imagery,” IEEE Trans. Geosci. Remote Sens., vol. 59, no. 5, pp. 3805–3815, 2021, doi: 10.1109/TGRS.2020.3017937.
[10] M. Azimi-Pour, H. Eskandari-Naddaf, and A. Pakzad, “Linear and non-linear SVM prediction for fresh properties and compressive strength of high volume fly ash self-compacting concrete,” Constr. Build. Mater., vol. 230, p. 117021, 2020, doi: 10.1016/j.conbuildmat.2019.117021.
[11] H. Al Azies, D. Trishnanti, and E. Mustikawati P.H, “Comparison of Kernel Support Vector Machine (SVM) in Classification of Human Development Index (HDI),” IPTEK J. Proc. Ser., vol. 0, no. 6, p. 53, 2019, doi: 10.12962/j23546026.y2019i6.6339.
[12] A. P. Gopi, R. N. S. Jyothi, V. L. Narayana, and K. S. Sandeep, “Classification of tweets data based on polarity using improved RBF kernel of SVM,” Int. J. Inf. Technol., 2020, doi: 10.1007/s41870-019-00409-4.
[13] S. Yenaeng, S. Saelee, and W. Samai, “Automatic Medical Case Study Essay Scoring by Support Vector Machine and Genetic Algorithms,” Int. J. Inf. Educ. Technol., vol. 4, no. 2, pp. 132–137, 2014, doi: 10.7763/ijiet.2014.v4.384.
[14] F. Budiman, “SVM-RBF parameters testing optimization using cross validation and grid search to improve multiclass classification,” Sci. Vis., vol. 11, no. 1, pp. 80–90, 2019, doi: 10.26583/sv.11.1.07.
[15] N. A. Al-Thanoon, O. S. Qasim, and Z. Y. Algamal, “A new hybrid firefly algorithm and particle swarm optimization for tuning parameter estimation in penalized support vector machine with application in chemometrics,” Chemom. Intell. Lab. Syst., vol. 184, no. December 2018, pp. 142–152, 2019, doi: 10.1016/j.chemolab.2018.12.003.
[16] N. B. Korade and M. Zuber, “Stock Price Forecasting using Convolutional Neural Networks and Optimization Techniques,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 11, pp. 378–385, 2022, doi: 10.14569/IJACSA.2022.0131142.
[17] R. Eberhart and J. Kennedy, “New optimizer using particle swarm theory,” Proc. Int. Symp. Micro Mach. Hum. Sci., pp. 39–43, 1995, doi: 10.1109/mhs.1995.494215.
[18] S. Panda and N. P. Padhy, “Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design,” Appl. Soft Comput. J., vol. 8, no. 4, pp. 1418–1427, 2008, doi: 10.1016/j.asoc.2007.10.009.
[19] F. Nzanywayingoma and Y. Yang, “Analysis of Particle Swarm Optimization and Genetic Algorithm based on Task Scheduling in Cloud Computing Environment,” Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 1, pp. 19–25, 2017, doi: 10.14569/ijacsa.2017.080104.
[20] Yoga Religia and A. Amali, “Perbandingan Optimasi Feature Selection pada Naïve Bayes untuk Klasifikasi Kepuasan Airline Passenger,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 3, pp. 527–533, 2021, doi: 10.29207/resti.v5i3.3086.
[21] I. Irawan, M. R. Qisthiano, M. Syahril, and P. M. Jakak, “Optimasi Prediksi Kelulusan Tepat Waktu: Studi Perbandingan Algoritma Random Forest dan Algoritma K-NN Berbasis PSO,” J. Pengemb. Sist. Inf. dan Inform., vol. 4, no. 4, pp. 26–35, 2023, doi: 10.47747/jpsii.v4i4.1374.
[22] S. Santoso and R. Nurmalina, “Perencanaan dan Pengembangan Aplikasi Absensi Mahasiswa Menggunakan Smart Card Guna Pengembangan Kampus Cerdas,” J. Integr., vol. 9, no. 1, p. 84, 2017, doi: 10.30871/ji.v9i1.288.
[23] J. Han, M. Kamber, and J. Pei, Data mining: Data mining concepts and techniques. 2011.
[24] O. A. Alrusaini, “Deep Learning Models for the Detection of Monkeypox Skin Lesion on Digital Skin Images,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 1, pp. 637–644, 2023, doi: 10.14569/IJACSA.2023.0140170.
[25] D. Berrar, “Cross-validation,” Encycl. Bioinforma. Comput. Biol. ABC Bioinforma., vol. 1–3, pp. 542–545, 2018, doi: 10.1016/B978-0-12-809633-8.20349-X.
[26] T. Fawcett, “An introduction to ROC analysis,” Pattern Recognit. Lett., vol. 27, no. 8, pp. 861–874, 2006, doi: 10.1016/j.patrec.2005.10.010.
[27] P. F. Gorunescu, Data Mining Concepts, Models and Techniques, vol. 59. 2011.
[28] E. S. Putri and M. Sadikin, “Prediksi Penjualan Produk Untuk Mengestimasi Kebutuhan Bahan Baku Menggunakan Perbandingan Algoritma LSTM dan ARIMA,” Format J. Ilm. Tek. Inform., vol. 10, no. 2, p. 162, 2021, doi: 10.22441/format.2021.v10.i2.007.
[29] S. Pothuganti, “Review On Over-fitting and Under-fitting Problems In Machine Learning and Solutions,” Int. J. Adv. Res. Electr. Electron. Instrum. Eng., vol. 7, no. 9, pp. 3692–3695, 2018, doi: 10.15662/IJAREEIE.2018.0709015.
[30] A. Montesinos López, Osval Antonio and J. Crossa, Multivariate Statistical Machine Learning Methods for Genomic Prediction. 2022.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 JUITA : Jurnal Informatika
This work is licensed under a Creative Commons Attribution 4.0 International License.
JUITA: Jurnal Informatika is licensed under a Creative Commons Attribution 4.0 International License.
