Prediksi Kepuasan dan Loyalitas Mahasiswa Menggunakan Random Forest dengan Pendekatan Explainable AI Berbasis SHAP

Indah Kusuma Dewi; Muhammad Ropianto; Elsha Dellista Ashofie

doi:10.35447/jitekh.v14i2.1591

Authors

Indah Kusuma Dewi Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Ibnu Sina, Batam
Muhammad Ropianto Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Ibnu Sina, Batam
Elsha Dellista Ashofie Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Ibnu Sina, Batam

DOI:

https://doi.org/10.35447/jitekh.v14i2.1591

Keywords:

Random Forest, Student Satisfaction, Student Loyalty, Explainable AI, SHAP

Abstract

Student satisfaction and loyalty are two strategic indicators of higher education institutions' success in maintaining educational service quality. This study builds prediction models for student satisfaction and loyalty using the Random Forest algorithm, interpreted transparently through the SHapley Additive exPlanations (SHAP) approach. Data were obtained from 437 student respondents covering eight service dimensions with 25 indicator variables. The satisfaction model achieved 94.32% accuracy (AUC = 0.8819), while the loyalty model obtained 87.50% accuracy (AUC = 0.5490). SHAP analysis identified Student financial difficulty solutions, Campus Wi-Fi stability, and Variation in teaching methods as dominant predictors of satisfaction, while Scholarship transparency is the main driver of loyalty.

Downloads

Download data is not yet available.

References

Q. Widayati, K. Adi, R. R. Isnanto, E. P. Agustini, D. R. R. Julianto, and F. B. Prakasa, “Predicting Student Loyalty in Higher Education Using Machine Learning: A Random Forest Approach,” Journal of Information Systems and Informatics, vol. 7, no. 1, pp. 63–77, Mar. 2025, doi: 10.51519/journalisi.v7i1.977.

M. de la C. Del Río-Rama, J. Álvarez-García, N. K. Mun, and A. Durán-Sánchez, “Influence of the Quality Perceived of Service of a Higher Education Center on the Loyalty of Students,” Front. Psychol., vol. 12, Jun. 2021, doi: 10.3389/fpsyg.2021.671407.

T. T. Borishade, O. O. Ogunnaike, O. Salau, B. D. Motilewa, and J. I. Dirisu, “Assessing the relationship among service quality, student satisfaction and loyalty: the NIGERIAN higher education experience,” Heliyon, vol. 7, no. 7, p. e07590, Jul. 2021, doi: 10.1016/j.heliyon.2021.e07590.

A. Fakhrudin, Nuryakin, I. Fatmawati, and M. Suyanto, “The Influence of Academic Service Quality on Student Loyalty: Mediating Effects of Student Satisfaction,” Amwaluna: Jurnal Ekonomi dan Keuangan Syariah, vol. 9, no. 1, pp. 69–83, 2025, doi: 10.29313/amwaluna.v6i2.3549.

H. V. Nguyen, T. D. Vu, M. Saleem, and A. Yaseen, “The influence of service quality on student satisfaction and student loyalty in Vietnam: the moderating role of the university image,” Journal of Trade Science, vol. 12, no. 1, pp. 37–59, Mar. 2024, doi: 10.1108/JTS-12-2023-0032.

S. Aghaei, Y. Shahbazi, M. Pirbabaei, and H. Beyti, “A hybrid SEM-neural network method for modeling the academic satisfaction factors of architecture students,” Computers and Education: Artificial Intelligence, vol. 4, p. 100122, Jan. 2023, doi: 10.1016/J.CAEAI.2023.100122.

Y. Luthfiana, S. Sujarwoto, and Moh. Said, “Evaluating Public University Service Quality and its Influence on Student Satisfaction and Loyalty: The Disjoint Two-Stage Approach,” Jurnal Manajemen Pelayanan Publik, vol. 9, no. 2, pp. 392–410, Jun. 2025, doi: 10.24198/jmpp.v9i2.61990.

M. Nachouki and M. A. Naaj, “Predicting Student Performance to Improve Academic Advising Using the Random Forest Algorithm,” International Journal of Distance Education Technologies, vol. 20, no. 1, Feb. 2022, doi: 10.4018/IJDET.296702.

M. Nachouki, E. A. Mohamed, R. Mehdi, and M. Abou Naaj, “Student course grade prediction using the random forest algorithm: Analysis of predictors’ importance,” Trends Neurosci. Educ., vol. 33, p. 100214, Dec. 2023, doi: 10.1016/J.TINE.2023.100214.

W. Saeed and C. Omlin, “Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities,” Knowl. Based. Syst., vol. 263, p. 110273, Mar. 2023, doi: 10.1016/J.KNOSYS.2023.110273.

S. M. Lundberg et al., “From local explanations to global understanding with explainable AI for trees,” Nat. Mach. Intell., vol. 2, no. 1, pp. 56–67, 2020, doi: 10.1038/s42256-019-0138-9.

F. Martínez-Roget, P. Freire Esparís, and E. Vázquez-Rozas, “University Student Satisfaction and Skill Acquisition: Evidence from the Undergraduate Dissertation,”Educ. Sci., vol. 10, no. 2, p. 29, 2020, doi: 10.3390/educsci10020029.

J. Li and X. Chen, “Modeling student satisfaction in online learning using random forest,” Sci. Rep., vol. 15, no. 1, Dec. 2025, doi: 10.1038/s41598-025-06686-3.

Y. Rimal, N. Sharma, and A. Alsadoon, “The accuracy of machine learning models relies on hyperparameter tuning: student result classification using random forest, randomized search, grid search, bayesian, genetic, and optuna algorithms,” Multimed. Tools Appl., vol. 83, no. 30, pp. 74349–74364, 2024, doi: 10.1007/s11042-024-18426-2.

J. G. C. Krüger, A. de S. Britto, and J. P. Barddal, “An explainable machine learning approach for student dropout prediction,” Expert Syst. Appl., vol. 233, p. 120933, Dec. 2023, doi: 10.1016/J.ESWA.2023.120933.

H. Khosravi et al., “Explainable Artificial Intelligence in education,” Computers and Education: Artificial Intelligence, vol. 3, p. 100074, Jan. 2022, doi: 10.1016/J.CAEAI.2022.100074.

R. Alamri and B. Alharbi, “Explainable Student Performance Prediction Models: A Systematic Review,” IEEE Access, vol. 9, pp. 33132–33143, 2021, doi: 10.1109/ACCESS.2021.3061368.

X. Duan, B. Pei, G. A. Ambrose, A. Hershkovitz, Y. Cheng, and C. Wang, “Towards transparent and trustworthy prediction of student learning achievement by including instructors as co-designers: a case study,” Educ. Inf. Technol. (Dordr)., vol. 29, no. 3, pp. 3075–3096, 2024, doi: 10.1007/s10639-023-11954-8.

V. W. Bwachele, Y.-L. Chong, and G. Krishnapillai, “Perceived service quality and student satisfaction in higher learning institutions in Tanzania,” Humanit. Soc. Sci. Commun., vol. 10, no. 1, p. 444, 2023, doi: 10.1057/s41599-023-01913-6.

M. Nagy and R. Molontay, “Interpretable Dropout Prediction: Towards XAI-Based Personalized Intervention,” Int. J. Artif. Intell. Educ., vol. 34, no. 2, pp. 274–300, Jun. 2024, doi: 10.1007/S40593-023-00331-8.

L. Breiman, “Random Forests,” Mach. Learn., vol. 45, no. 1, pp. 5–32, Oct. 2001, doi: 10.1023/A:1010933404324.

D. Delen, “A comparative analysis of machine learning techniques for student retention management,” Decis. Support Syst., vol. 49, no. 4, pp. 498–506, Nov. 2010, doi: 10.1016/j.dss.2010.06.003.

C. Romero and S. Ventura, “Educational data mining and learning analytics: An updated survey,” WIREs Data Min. Knowl. Discov., vol. 10, no. 3, p. e1355, 2020, doi: 10.1002/widm.1355.

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique,” J. Artif. Intell. Res., vol. 16, pp. 321–357, 2002, doi: 10.1613/jair.953.

T. Saito and M. Rehmsmeier, “The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets,” PLoS One, vol. 10, no. 3, p. e0118432, Mar. 2015, doi: 10.1371/journal.pone.0118432.

A. Barredo Arrieta et al., “Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Inf. Fusion, vol. 58, pp. 82–115, 2020, doi: 10.1016/j.inffus.2019.12.012.