A Hybrid SEM-PLS and ANN Approach for Predicting Student Loyalty in Higher Education Learning Management Systems

Authors

  • Hamidah Institut Sains dan Bisnis Atma Luhur, Indonesia
  • Sarwindah Institut Sains dan Bisnis Atma Luhur, Indonesia
  • Hengki Institut Sains dan Bisnis Atma Luhur, Indonesia
  • Tri Sugihartono Institut Sains dan Bisnis Atma Luhur, Indonesia
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DOI:

https://doi.org/10.63158/journalisi.v8i3.1625

Keywords:

Learning Management System, Student Satisfaction, Student Loyalty, SEM-PLS, Multilayer Perceptron, Hybrid Modelling

Abstract

This study aims to develop a hybrid Structural Equation Modeling–Partial Least Squares (SEM-PLS) and Artificial Neural Network (ANN) approach to analyze student loyalty in Learning Management Systems (LMS) at ISB Atma Luhur. Data were collected from 200 students at ISB Atma Luhur, representing a single-institution sample, and analyzed using SEM-PLS to examine causal relationships and ANN (Multilayer Perceptron) implemented in SPSS to support predictive analysis. The model includes e-service quality, user experience, information quality, and system quality as predictors of satisfaction and loyalty. The SEM-PLS results show that E-Service Quality (β = 0.350), System Quality (β = 0.170), and User Experience (β = 0.292) significantly affect Satisfaction, whereas Information Quality is not statistically significant (p = 0.054). Satisfaction positively influences Loyalty (β = 0.360), and User Experience has the strongest direct effect on Loyalty (β = 0.484). The model explains a substantial proportion of variance (R² = 0.717 and 0.631) with positive Q² values (0.460 and 0.379). Across ten independent runs, the ANN model achieved an average accuracy of 84.88% (SD = 2.82) and an average AUC of 0.949 (SD = 0.003), indicating stable predictive performance, indicating promising predictive performance under the current testing configuration. The findings provide context-specific explanatory and predictive insights into student loyalty in LMS, however, they should be interpreted with caution due to discriminant-validity limitations and the single-institution setting of the study.

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References

[1] Y. Luthfiana, S. Sujarwoto, and M. Said, “Strategic Insights to Enhance Student Loyalty Through Service Quality and Satisfaction: Importance-Performance Map Analysis,” J. Ilm. Adm. Publik, vol. 011, no. 03, pp. 322–336, 2025, doi: 10.21776/ub.jiap.2025.011.03.5.

[2] M. N. Ayubi and A. Retnowardhani, “Optimizing Learning Experiences: A Study of Student Satisfaction with LMS in Higher Education,” APTISI Trans. Technopreneursh., vol. 7, no. 2, pp. 527–541, 2025, doi: 10.34306/att.v7i2.501.

[3] F. D. Mohd Nasir, M. A. M. Hussain, H. Mohamed, M. A. Mohd Mokhtar, and N. A. Karim, “Student Satisfaction in Using a Learning Management System (LMS) for Blended Learning Courses for Tertiary Education,” Asian J. Univ. Educ., vol. 17, no. 4, pp. 442–454, 2021, doi: 10.24191/ajue.v17i4.16225.

[4] A. P. Sari and C. Harito, “Analysis of Customer Loyalty and Satisfaction Using Structural Equation Modeling (SEM) Approach,” J. Penelit. Pendidik. IPA, vol. 11, no. 5, pp. 986–992, 2025, doi: 10.29303/jppipa.v11i5.10520.

[5] B. Tunca, “Structural Equation Modelling and Multivariate Research (Smmr) Hybrid Use of Structural Equation Modeling and Machine Learning: Literature Review and Future Potential,” SEMMR J., vol. 2, no. 1, pp. 1–23, 2025, doi: 10.5281/Zenodo.15740696.

[6] J. Hair, “When to Use and How to Report the Results of PLS-SEM,” Ind. Manag. Data Syst., vol. 31, no. 1, pp. 1–25, 2019, doi: 10.1108/IMDS-10-2018-0449.

[7] K. B. Desta, L. Msengana, and K. B. Desta, “Integrating PLS-SEM and ANN to explore major resilience factors of construction Projects : The Case of Ethiopia Integrating PLS-SEM and ANN to explore major resilience factors of construction Projects : The Case of Ethiopia,” Bus. Manag. Sci. Int. Q., vol. 16, no. 3, pp. 766–802, 2025, doi: 10.13132/2038-5498/16.3.765-802.

[8] H. P. Learn, “Research on the Effectiveness of Learning Management Systems (LMS) Use in Higher Education,” Int. J. Corner Educ. Res., vol. 4, no. 1, pp. 20–29, 2000, doi: 10.54012/ijcer.v4i1.625.

[9] A. Boodaghian Asl, J. Raghothama, A. Darwich, and S. Meijer, “A hybrid modeling approach to simulate complex systems and classify behaviors,” Netw. Model. Anal. Heal. Informatics Bioinforma., vol. 13, no. 1, pp. 1–12, 2024, doi: 10.1007/s13721-024-00446-5.

[10] Hamidah, O. Rizan, D. Wahyuningsih, H. A. Pradana, and S. Ramadella, “SAW Method in Supporting the Process of Admission of New Junior High School Students,” 2020 8th Int. Conf. Cyber IT Serv. Manag. CITSM 2020, 2020, doi: 10.1109/CITSM50537.2020.9268874.

[11] N. F. Richter and A. A. Tudoran, “Elevating theoretical insight and predictive accuracy in business research: Combining PLS-SEM and selected machine learning algorithms,” J. Bus. Res., vol. 173, no. November 2023, p. 114453, 2024, doi: 10.1016/j.jbusres.2023.114453.

[12] A. M. Schweidtmann, D. Zhang, and M. von Stosch, “A review and perspective on hybrid modeling methodologies,” Digit. Chem. Eng., vol. 10, no. October 2023, p. 100136, 2024, doi: 10.1016/j.dche.2023.100136.

[13] Z. Kanetaki, C. Stergiou, G. Bekas, C. Troussas, and C. Sgouropoulou, “A Hybrid Machine Learning Model for Grade Prediction in Online Engineering Education,” Int. J. Eng. Pedagog., vol. 12, no. 3, pp. 4–23, 2022, doi: 10.3991/IJEP.V12I3.23873.

[14] S. H. Gulo and A. H. Lubis, “Penerapan Multi-Layer Perceptron untuk Mengklasifikasi Penduduk Kurang Mampu,” Explorer (Hayward)., vol. 4, no. 2, pp. 51–59, 2024, doi: 10.47065/explorer.v4i2.1146.

[15] R. Ardi, A. N. A. Ms, R. R. Amalia, and T. N. Zahari, “A Hybrid SEM-ANN Model for Predicting Purchase Intention Toward Recycled PET Products: Evidence from Indonesian Generational Segments,” Int. J. Technol., vol. 17, no. 1, pp. 69–81, 2026, doi: 10.14716/ijtech.v17i1.8209.

[16] C. Saksupawattanakul and W. Vatanawood, “Predictive Modeling of Software Behavior Using Machine Learning,” IEEE Access, vol. 12, no. September, pp. 120584–120596, 2024, doi: 10.1109/ACCESS.2024.3451012.

[17] H. Q. Yousaf, S. Rehman, M. Ahmed, and S. Munawar, “Investigating students’ satisfaction in online learning: the role of students’ interaction and engagement in universities,” Interact. Learn. Environ., vol. 31, no. 10, pp. 7104–7121, 2023, doi: 10.1080/10494820.2022.2061009.

[18] T. Chandra, M. Ng, S. Chandra, and Priyono, “The effect of service quality on student satisfaction and student loyalty: An empirical study,” J. Soc. Stud. Educ. Res., vol. 26, no. 2, pp. 179–199, 2022, doi: 10.17499/jsser.12590.

[19] A. M. Karim Amrullah, S. Bayramov, A. Aziz, and A. Haris, “Evaluating the Impact of Learning Management System Usage on Student Satisfaction and Learning Outcomes at Universitas Islam Negeri (UIN) Maulana Malik Ibrahim During the COVID-19 Pandemic,” Glob. Educ. Res. Rev., vol. 1, no. 1, pp. 38–48, 2024, doi: 10.71380/gerr-04-2024-7.

[20] S. K. Munabi, J. Aguti, and H. M. Nabushawo, “Using the TAM Model to Predict Undergraduate Distance Learners Behavioural Intention to Use the Makerere University Learning Management System,” OALib, vol. 07, no. 09, pp. 1–12, 2020, doi: 10.4236/oalib.1106699.

[21] A. Tarhini, R. M. Deh, K. A. Al-Busaidi, A. B. Mohammed, and M. Maqableh, “Factors influencing students’ adoption of e-learning: A structural equation modeling approach,” J. Int. Educ. Bus., vol. 10, no. 2, pp. 164–182, 2021, doi: 10.1108/JIEB-09-2021-0032.

[22] Y. R. Daud, M. R. bin Mohd Amin, and J. bin Abdul Karim, “Antecedents of student loyalty in open and distance learning institutions: An empirical analysis,” Int. Rev. Res. Open Distrib. Learn., vol. 21, no. 3, pp. 18–40, 2020, doi: 10.19173/irrodl.v21i3.4590.

[23] H. B. Seta, T. Wati, A. Muliawati, and A. N. Hidayanto, “E-learning success model: An extention of delone & mclean is’ success model,” Indones. J. Electr. Eng. Informatics, vol. 6, no. 3, p. 281~291, 2021, doi: 10.11591/ijeei.v6i3.505.

[24] C. Arabella, L. Mani, F. C. Sahabu, and M. Aras, “Customer satisfaction and loyalty in the digital era: a survey on a leading travel startup application in Indonesia,” Multidiscip. Sci. J., vol. 7, no. 9, pp. 1–12, 2025, doi: 10.31893/multiscience.2025446.

[25] R. Novyantri and M. Setiawardani, “The Effect Of E-Service Quality On Customer Loyalty With Customer Satisfaction As,” Int. J. Adm. , Bus. Organ., vol. 2, no. 3, pp. 49–58, 2021, doi: 10.61242/ijabo.21.174.

[26] L. Handayani, “A Machine Learning-Based Early Warning System for Student Performance Prediction : System Development and Empirical Evaluation in Higher Education,” J. Artif. Intell. Inf. Technol., vol. 1, no. 1, pp. 171–190, 2026, doi: 10.51903/92j5wj58.

[27] S. Yarsasi, I. Tahyudin, and T. Hariguna, “User Experience Analysis of Learning Management System ( LMS ) SINAU to Support Learning with MERDEKA Flow Using UX Curve Method,” J. Tek. Inform., vol. 7, no. 1, pp. 110–125, 2026, doi: 10.52436/1.jutif.2026.7.1.4579.

[28] I. Maslov, S. Nikou, and P. Hansen, “Exploring user experience of learning management system,” Int. J. Inf. Learn. Technol., vol. 38, no. 4, pp. 344–363, 2021, doi: 10.1108/IJILT-03-2021-0046.

[29] T. Gondomulio and J. S. Suroso, “User Satisfaction Evaluation of E-Learning as a Learning System at Heritage School,” J. Sist. Cerdas, vol. 6, no. 2, pp. 77–90, 2023, doi: 10.37396/jsc.v6i2.285.

[30] A. Albahri, “Hybrid Artificial Intelligence Models for Educational Prediction,” Expert Syst. Appl., vol. 190, no. 1, 2022, doi: 10.1016/j.eswa.2021.116126.

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Published

2026-06-23

Issue

Vol. 8 No. 3 (2026): June

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Articles

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