Seleksi Fitur pada Supervised Learning: Klasifikasi Prestasi Belajar Mahasiswa Saat dan Pasca Pandemi COVID-19
(1) Universitas Islam Negeri Sultan Syarif Kasim Riau
(2) Universitas Islam Negeri Sultan Syarif Kasim Riau
(*) Corresponding Author
Abstrak
Kata Kunci
Teks Lengkap:
PDFReferensi
[1] A. Cahyadi, Hendryadi, S. Widyastuti, V. N. Mufidah, and Achmadi, “Emergency remote teaching evaluation of the higher education in Indonesia,” Heliyon, vol. 7, no. 8, 2021, doi: 10.1016/j.heliyon.2021.e07788.
[2] N. Aisha and A. Ratra, “Online education amid COVID-19 pandemic and its opportunities, challenges and psychological impacts among students and teachers: a systematic review,” Asian Assoc. Open Univ. J., vol. 17, no. 3, pp. 242–260, 2022, doi: 10.1108/AAOUJ-03-2022-0028.
[3] W. Rahayu, M. D. K. Putra, Faturochman, Meiliasari, E. Sulaeman, and R. B. Koul, “Development and validation of Online Classroom Learning Environment Inventory (OCLEI): The case of Indonesia during the COVID-19 pandemic,” Learn. Environ. Res., vol. 25, no. 1, pp. 97–113, 2022, doi: 10.1007/s10984-021-09352-3.
[4] G. H. Mardini and O. A. Mah’d, “Distance learning as emergency remote teaching vs. traditional learning for accounting students during the COVID-19 pandemic: Cross-country evidence,” J. Account. Educ., vol. 61, p. 100814, 2022, doi: 10.1016/j.jaccedu.2022.100814.
[5] M. Al-Nasa’h, L. Al-Tarawneh, F. M. Abu Awwad, and I. Ahmad, “Estimating students’ online learning satisfaction during COVID-19: A discriminant analysis,” Heliyon, vol. 7, no. 12, p. e08544, 2021, doi: 10.1016/j.heliyon.2021.e08544.
[6] L. Anthonysamy and P. Singh, “The impact of satisfaction, and autonomous learning strategies use on scholastic achievement during Covid-19 confinement in Malaysia,” Heliyon, vol. 9, no. 2, p. e12198, 2023, doi: 10.1016/j.heliyon.2022.e12198.
[7] S. Ali, Y. Hafeez, M. A. Abbas, M. Aqib, and A. Nawaz, “Enabling remote learning system for virtual personalized preferences during COVID-19 pandemic,” Multimed. Tools Appl., vol. 80, no. 24, pp. 33329–33355, 2021, doi: 10.1007/s11042-021-11414-w.
[8] S. Adewale and M. B. Tahir, “Virtual learning environment factors as predictors of students’ learning satisfaction during COVID-19 period in Nigeria,” Asian Assoc. Open Univ. J., vol. 17, no. 2, pp. 120–133, 2022, doi: 10.1108/AAOUJ-10-2021-0121.
[9] W. Wagiran, S. Suharjana, M. Nurtanto, and F. Mutohhari, “Determining the e-learning readiness of higher education students: A study during the COVID-19 pandemic,” Heliyon, vol. 8, no. 10, p. e11160, 2022, doi: 10.1016/j.heliyon.2022.e11160.
[10] J. Melgaard, R. Monir, L. A. Lasrado, and A. Fagerstrøm, “Academic Procrastination and Online Learning during the COVID-19 Pandemic,” Procedia Comput. Sci., vol. 196, no. 2021, pp. 117–124, 2021, doi: 10.1016/j.procs.2021.11.080.
[11] A. Cahyadi, Hendryadi, S. Widyastuti, and Suryani, “COVID-19, emergency remote teaching evaluation: the case of Indonesia,” Educ. Inf. Technol., vol. 27, no. 2, pp. 2165–2179, 2022, doi: 10.1007/s10639-021-10680-3.
[12] F. Abdullah and S. Kauser, “Students’ perspective on online learning during pandemic in higher education,” Qual. Quant., no. 0123456789, 2022, doi: 10.1007/s11135-022-01470-1.
[13] M. Kerres and J. Buchner, “Education after the Pandemic: What We Have (Not) Learned about Learning,” Educ. Sci., vol. 12, no. 5, 2022, doi: 10.3390/educsci12050315.
[14] S. I. N. W. Abdullah, K. Arokiyasamy, S. L. Goh, A. J. Culas, and N. M. A. Manaf, “University students’ satisfaction and future outlook towards forced remote learning during a global pandemic,” Smart Learn. Environ., vol. 9, no. 1, 2022, doi: 10.1186/s40561-022-00197-8.
[15] Y. Zhu, G. Geng, L. Disney, and Z. Pan, Changes in university students’ behavioral intention to learn online throughout the COVID-19: Insights for online teaching in the post-pandemic era, no. 0123456789. Springer US, 2022.
[16] C. Rapanta, L. Botturi, P. Goodyear, L. Guàrdia, and M. Koole, “Balancing Technology, Pedagogy and the New Normal: Post-pandemic Challenges for Higher Education,” Postdigital Sci. Educ., vol. 3, no. 3, pp. 715–742, 2021, doi: 10.1007/s42438-021-00249-1.
[17] A. Patricia Aguilera-Hermida, “College students’ use and acceptance of emergency online learning due to COVID-19,” Int. J. Educ. Res. Open, vol. 1, no. July, p. 100011, 2020, doi: 10.1016/j.ijedro.2020.100011.
[18] V. Ratten, “The post COVID-19 pandemic era: Changes in teaching and learning methods for management educators,” Int. J. Manag. Educ., vol. 21, no. 2, p. 100777, 2023, doi: 10.1016/j.ijme.2023.100777.
[19] A. Sharma and I. Alvi, “Evaluating pre and post COVID 19 learning: An empirical study of learners’ perception in higher education,” Educ. Inf. Technol., vol. 26, no. 6, pp. 7015–7032, 2021, doi: 10.1007/s10639-021-10521-3.
[20] N. Yan and O. T. S. Au, “Online learning behavior analysis based on machine learning,” Asian Assoc. Open Univ. J., vol. 14, no. 2, pp. 97–106, 2019, doi: 10.1108/AAOUJ-08-2019-0029.
[21] A. Dinesh Kumar, R. Pandi Selvam, and V. Palanisamy, “Hybrid Classification Algorithms for Predicting Student Performance,” in 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 2021, pp. 1074–1079, doi: 10.1109/ICAIS50930.2021.9395974.
[22] R. Patil, S. Salunke, M. Kalbhor, and R. Lomte, “Prediction System for Student Performance Using Data Mining Classification,” in 2018 4th International Conference on Computing, Communication Control and Automation (ICCUBEA), 2018, pp. 1–4, doi: 10.1109/ICCUBEA.2018.8697770.
[23] C. C. Kiu, “Data Mining Analysis on Student’s Academic Performance through Exploration of Student’s Background and Social Activities,” in 2018 4th International Conference on Advances in Computing, Communication and Automation (ICACCA), 2018, pp. 1–5, doi: 10.1109/ICACCAF.2018.8776809.
[24] A. Zamir et al., “Phishing web site detection using diverse machine learning algorithms,” Electron. Libr., vol. 38, no. 1, pp. 65–80, 2020, doi: 10.1108/EL-05-2019-0118.
[25] V. Jackins, S. Vimal, M. Kaliappan, and M. Y. Lee, “AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes,” J. Supercomput., vol. 77, no. 5, pp. 5198–5219, 2021, doi: 10.1007/s11227-020-03481-x.
[26] K. Alpan and G. S. Ilgi, “Classification of Diabetes Dataset with Data Mining Techniques by Using WEKA Approach,” in 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2020, pp. 1–7, doi: 10.1109/ISMSIT50672.2020.9254720.
[27] M. Ozcan and S. Peker, “A classification and regression tree algorithm for heart disease modeling and prediction,” Healthc. Anal., vol. 3, no. July 2022, p. 100130, 2023, doi: 10.1016/j.health.2022.100130.
[28] P. Subarkah, A. N. Ikhsan, and A. Setyanto, “The effect of the number of attributes on the selection of study program using classification and regression trees algorithms,” in 2018 3rd International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 2018, pp. 1–5, doi: 10.1109/ICITISEE.2018.8721030.
[29] S. N. Lathifah, F. Nhita, A. Aditsania, and D. Saepudin, “Rainfall forecasting using the classification and regression tree (CART) algorithm and adaptive synthetic sampling (study case: Bandung regency),” in 7th International Conference on Information and Communication Technology (ICoICT), 2019, pp. 1–5, doi: 10.1109/ICoICT.2019.8835308.
[30] S. İlkin, T. H. Gençtürk, F. Kaya Gülağız, H. Özcan, M. A. Altuncu, and S. Şahin, “hybSVM: Bacterial colony optimization algorithm based SVM for malignant melanoma detection,” Eng. Sci. Technol. an Int. J., vol. 24, no. 5, pp. 1059–1071, 2021, doi: 10.1016/j.jestch.2021.02.002.
[31] A. Mahmood and H. U. Khan, “Identification of critical factors for assessing the quality of restaurants using data mining approaches,” Electron. Libr., vol. 37, no. 6, pp. 952–969, 2019, doi: 10.1108/EL-12-2018-0241.
[32] M. J. Nayeem, S. Rana, F. Alam, and M. A. Rahman, “Prediction of Hepatitis Disease Using K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Multi-Layer Perceptron and Random Forest,” in 2021 International Conference on Information and Communication Technology for Sustainable Development, (ICICT4SD), 2021, pp. 280–284, doi: 10.1109/ICICT4SD50815.2021.9397013.
[33] G. Ramaswami, T. Susnjak, A. Mathrani, J. Lim, and P. Garcia, “Using educational data mining techniques to increase the prediction accuracy of student academic performance,” Inf. Learn. Sci., vol. 120, no. 7–8, pp. 451–467, 2019, doi: 10.1108/ILS-03-2019-0017.
[34] V. Chang, V. R. Bhavani, A. Q. Xu, and M. Hossain, “An artificial intelligence model for heart disease detection using machine learning algorithms,” Healthc. Anal., vol. 2, p. 100016, 2022, doi: 10.1016/j.health.2022.100016.
[35] Islamiyah, A. N. Afiyah, N. Dengen, and M. Taruk, “Comparison Performance of C4.5 , Naïve Bayes and K-Nearest Neighbor in Determination Drug Rehabilitation,” in 2019 5th International Conference on Science in Information Technology (ICSITech), 2019, pp. 112–117, doi: 10.1109/ICSITech46713.2019.8987455.
[36] R. S. Raj, D. S. Sanjay, M. Kusuma, and S. Sampath, “Comparison of Support Vector Machine and Naïve Bayes Classifiers for Predicting Diabetes,” in 2019 1st International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering (ICATIECE), 2019, pp. 41–45, doi: 10.1109/ICATIECE45860.2019.9063792.
[37] D. H. Jeong, B. K. Jeong, N. Leslie, C. Kamhoua, and S.-Y. Ji, “Designing a supervised feature selection technique for mixed attribute data analysis,” Mach. Learn. with Appl., vol. 10, no. March, p. 100431, 2022, doi: 10.1016/j.mlwa.2022.100431.
[38] H. Zhou, X. Wang, and Y. Zhang, “Feature selection based on weighted conditional mutual information,” Appl. Comput. Informatics, 2020, doi: 10.1016/j.aci.2019.12.003.
[39] H. Zhou, X. Wang, and R. Zhu, “Feature selection based on mutual information with correlation coefficient,” Appl. Intell., vol. 52, no. 5, pp. 5457–5474, 2022, doi: 10.1007/s10489-021-02524-x.
[40] F. Macedo, R. Valadas, E. Carrasquinha, M. R. Oliveira, and A. Pacheco, “Feature selection using Decomposed Mutual Information Maximization,” Neurocomputing, vol. 513, pp. 215–232, 2022, doi: 10.1016/j.neucom.2022.09.101.
[41] J. Gonzalez-Lopez, S. Ventura, and A. Cano, “Distributed multi-label feature selection using individual mutual information measures,” Knowledge-Based Syst., vol. 188, 2020, doi: 10.1016/j.knosys.2019.105052.
[42] K. Gajowniczek, J. Wu, S. Gupta, and C. Bajaj, “HOFS: Higher order mutual information approximation for feature selection in R,” SoftwareX, vol. 19, p. 101148, 2022, doi: 10.1016/j.softx.2022.101148.
[43] H. Chauhan, K. Modi, and S. Shrivastava, “Development of a classifier with analysis of feature selection methods for COVID-19 diagnosis,” World J. Eng., vol. 19, no. 1, pp. 49–57, 2022, doi: 10.1108/WJE-10-2020-0537.
[44] F. Souza, C. Premebida, and R. Araújo, “High-order conditional mutual information maximization for dealing with high-order dependencies in feature selection,” Pattern Recognit., vol. 131, p. 108895, 2022, doi: 10.1016/j.patcog.2022.108895.
[45] T. Bhadra, S. Mallik, N. Hasan, and Z. Zhao, “Comparison of five supervised feature selection algorithms leading to top features and gene signatures from multi-omics data in cancer,” BMC Bioinformatics, vol. 23, pp. 1–18, 2022, doi: 10.1186/s12859-022-04678-y.
[46] S. Williamson, K. Vijayakumar, and V. J. Kadam, “Predicting breast cancer biopsy outcomes from BI-RADS findings using random forests with chi-square and MI features,” Multimed. Tools Appl., vol. 81, no. 26, pp. 36869–36889, 2022, doi: 10.1007/s11042-021-11114-5.
[47] N. Peker and C. Kubat, “Application of Chi-square discretization algorithms to ensemble classification methods,” Expert Syst. Appl., vol. 185, no. June 2020, p. 115540, 2021, doi: 10.1016/j.eswa.2021.115540.
[48] H. M. Abdelaal, B. R. Elemary, and H. A. Youness, “Classification of Hadith According to Its Content Based on Supervised Learning Algorithms,” IEEE Access, vol. 7, pp. 152379–152387, 2019, doi: 10.1109/ACCESS.2019.2948159.
[49] I. Sumaiya Thaseen and C. Aswani Kumar, “Intrusion detection model using fusion of chi-square feature selection and multi class SVM,” J. King Saud Univ. - Comput. Inf. Sci., vol. 29, no. 4, pp. 462–472, 2017, doi: 10.1016/j.jksuci.2015.12.004.
[50] R. Spencer, F. Thabtah, N. Abdelhamid, and M. Thompson, “Exploring feature selection and classification methods for predicting heart disease,” Digit. Heal., vol. 6, pp. 1–10, 2020, doi: 10.1177/2055207620914777.
[51] V. Sharma and K. C. Juglan, “Automated Classification of Fatty and Normal Liver Ultrasound Images Based on Mutual Information Feature Selection,” IRBM, vol. 39, no. 5, pp. 313–323, 2018, doi: 10.1016/j.irbm.2018.09.006.
[52] S. Kumari, D. Kumar, and M. Mittal, “An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier,” Int. J. Cogn. Comput. Eng., vol. 2, pp. 40–46, 2021, doi: 10.1016/j.ijcce.2021.01.001.
[53] W. BinSaeedan and S. Alramlawi, “CS-BPSO: Hybrid feature selection based on chi-square and binary PSO algorithm for Arabic email authorship analysis,” Knowledge-Based Syst., vol. 227, p. 107224, 2021, doi: 10.1016/j.knosys.2021.107224.
[54] D. Effrosynidis and A. Arampatzis, “An evaluation of feature selection methods for environmental data,” Ecol. Inform., vol. 61, no. January, p. 101224, 2021, doi: 10.1016/j.ecoinf.2021.101224.
[55] G. Dimic, D. Rancic, N. Macek, P. Spalevic, and V. Drasute, “Improving the prediction accuracy in blended learning environment using synthetic minority oversampling technique,” Inf. Discov. Deliv., vol. 47, no. 2, pp. 76–83, 2019, doi: 10.1108/IDD-08-2018-0036.
[56] A. Madasu and S. Elango, “Efficient feature selection techniques for sentiment analysis,” Multimed. Tools Appl., vol. 79, no. 9–10, pp. 6313–6335, 2020, doi: 10.1007/s11042-019-08409-z.
[57] S. Mochammad, Y. J. Kang, Y. Noh, S. Park, and B. Ahn, “Stable Hybrid Feature Selection Method for Compressor Fault Diagnosis,” IEEE Access, vol. 9, pp. 97415–97429, 2021, doi: 10.1109/ACCESS.2021.3092884.
[58] A. Kaur, K. Guleria, and N. K. Trivedi, “Feature Selection in Machine Learning: Methods and Comparison,” in 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), 2021, pp. 789–795, doi: 10.1109/ICACITE51222.2021.9404623.
[59] H. Utama, “Sentiment analysis in airline tweets using mutual information for feature selection,” in 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (CITISEE), 2019, pp. 295–300, doi: 10.1109/ICITISEE48480.2019.9003903.
[60] M. Alduailij, Q. W. Khan, M. Tahir, M. Sardaraz, M. Alduailij, and F. Malik, “Machine-Learning-Based DDoS Attack Detection Using Mutual Information and Random Forest Feature Importance Method,” Symmetry (Basel)., vol. 14, no. 6, pp. 1–15, 2022, doi: 10.3390/sym14061095.
[61] K. Wang, W. Mao, W. Feng, and H. Wang, “Research on spam filtering technology based on new mutual information feature selection algorithm,” J. Phys. Conf. Ser., vol. 1673, no. 1, 2020, doi: 10.1088/1742-6596/1673/1/012028.
[62] M. Malekipirbazari, V. Aksakalli, W. Shafqat, and A. Eberhard, “Performance comparison of feature selection and extraction methods with random instance selection,” Expert Syst. Appl., vol. 179, no. April, p. 115072, 2021, doi: 10.1016/j.eswa.2021.115072.
[63] T. H. Kerbaa, A. Mezache, and H. Oudira, “Model Selection of Sea Clutter Using Cross Validation Method,” in Procedia Computer Science, 2019, vol. 158, pp. 394–400, doi: 10.1016/j.procs.2019.09.067.
[64] O. Karal, “Performance comparison of different kernel functions in SVM for different k value in k-fold cross-validation,” in 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), 2020, pp. 1–5, doi: 10.1109/ASYU50717.2020.9259880.
[65] S. Saud, B. Jamil, Y. Upadhyay, and K. Irshad, “Performance improvement of empirical models for estimation of global solar radiation in India: A k-fold cross-validation approach,” Sustain. Energy Technol. Assessments, vol. 40, p. 100768, 2020, doi: 10.1016/j.seta.2020.100768.
[66] T. T. S. Nguyen and P. M. T. Do, “Classification optimization for training a large dataset with Naïve Bayes,” J. Comb. Optim., vol. 40, no. 1, pp. 141–169, 2020, doi: 10.1007/s10878-020-00578-0.
[67] P. Kamal and S. Ahuja, “An ensemble-based model for prediction of academic performance of students in undergrad professional course,” J. Eng. Des. Technol., vol. 17, no. 4, pp. 769–781, 2019, doi: 10.1108/JEDT-11-2018-0204.
[68] H. F. Zhou, J. W. Zhang, Y. Q. Zhou, X. J. Guo, and Y. M. Ma, “A feature selection algorithm of decision tree based on feature weight,” Expert Syst. Appl., vol. 164, no. February 2020, p. 113842, 2021, doi: 10.1016/j.eswa.2020.113842.
[69] E. Z. Aziza, L. Mohamed El Amine, M. Mohamed, and B. Abdelhafid, “Decision tree CART algorithm for diabetic retinopathy classification,” in 2019 6th International Conference on Image and Signal Processing and their Applications (ISPA), 2019, pp. 1–5, doi: 10.1109/ISPA48434.2019.8966905.
[70] E. Pekel Özmen and T. Özcan, “Diagnosis of diabetes mellitus using artificial neural network and classification and regression tree optimized with genetic algorithm,” J. Forecast., vol. 39, no. 4, pp. 661–670, 2020, doi: 10.1002/for.2652.
[71] S. Cano-Ortiz, P. Pascual-Muñoz, and D. Castro-Fresno, “Machine learning algorithms for monitoring pavement performance,” Autom. Constr., vol. 139, no. September 2021, 2022, doi: 10.1016/j.autcon.2022.104309.
[72] N. Rust-Nguyen, S. Sharma, and M. Stamp, “Darknet traffic classification and adversarial attacks using machine learning,” Comput. Secur., vol. 127, p. 103098, 2023, doi: 10.1016/j.cose.2023.103098.
[73] K. Iqbal and M. S. Khan, “Email classification analysis using machine learning techniques,” Appl. Comput. Informatics, 2022, doi: 10.1108/ACI-01-2022-0012.
[74] L. A. C. Ahakonye, C. I. Nwakanma, J. M. Lee, and D. S. Kim, “SCADA intrusion detection scheme exploiting the fusion of modified decision tree and Chi-square feature selection,” Internet of Things (Netherlands), vol. 21, no. August 2022, p. 100676, 2023, doi: 10.1016/j.iot.2022.100676.
[75] F. Itoo, Meenakshi, and S. Singh, “Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection,” Int. J. Inf. Technol., vol. 13, no. 4, pp. 1503–1511, 2021, doi: 10.1007/s41870-020-00430-y.
[76] F. Thabtah, F. Kamalov, S. Hammoud, and S. R. Shahamiri, “Least Loss: A simplified filter method for feature selection,” Inf. Sci. (Ny)., vol. 534, pp. 1–15, 2020, doi: 10.1016/j.ins.2020.05.017.
[77] M. Jansi Rani and D. Devaraj, “Two-Stage Hybrid Gene Selection Using Mutual Information and Genetic Algorithm for Cancer Data Classification,” J. Med. Syst., vol. 43, no. 8, 2019, doi: 10.1007/s10916-019-1372-8.
[78] M. S. Al-Batah, M. Alzyoud, R. Alazaidah, M. Toubat, H. Alzoubi, and A. Olaiyat, “Early Prediction of Cervical Cancer Using Machine Learning Techniques,” Jordanian J. Comput. Inf. Technol., vol. 08, no. 04, pp. 357–369, 2022, doi: 10.5455/jjcit.71-1661691447.
Artikel Statistik
PDF telah dilihat : 528 kali
Refbacks
- Saat ini tidak ada refbacks.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
 | Alamat Redaksi : Departemen Sistem Informasi, Fakultas Teknologi Informasi Universitas Andalas Kampus Limau Manis, Padang 25163, Sumatera Barat email: teknosi@fti.unand.ac.id |
Jumlah Pengunjung :
This work by JSI-Unand and licensed under a CC BY-SA 4.0 International License.