A Novel Hybrid Multi-Criteria Decision and Data Mining Framework for Educational Intelligence Systems

Orissa Octaria; Danny Manongga; Irwan Sembiring

doi:10.48084/etasr.18583

Authors

Orissa Octaria Doctoral Program in Computer Science, Satya Wacana Christian University, Indonesia | Faculty of Computer Science and Engineering, Universitas Multi Data Palembang, Indonesia
Danny Manongga Faculty of Information Technology, Satya Wacana Christian University, Indonesia
Irwan Sembiring Faculty of Information Technology, Satya Wacana Christian University, Indonesia

Volume: 16 | Issue: 3 | Pages: 36608-36616 | June 2026 | https://doi.org/10.48084/etasr.18583

Received: 7 March 2026 | Revised: 31 March 2026, 21 April 2026, and 4 May 2026 | Accepted: 8 May 2026 | Online: 18 May 2026

Corresponding author: Orissa Octaria

Abstract

Although student exchange programs provide substantial advantages, a large number of university students are still unaware that such opportunities exist. This study addresses this gap by proposing a hybrid Educational Intelligence System (EIS), which is a data-driven decision-support framework that integrates Multi-Criteria Decision Analysis (MCDA) with data mining techniques. Specifically, the Analytical Hierarchy Process (AHP) is adopted as the MCDA method. Using AHP, the relative significance of each criterion influencing student awareness is determined. Concurrently, data mining methods, namely, clustering and classification are employed to reveal underlying patterns within student data. Clustering serves to categorize students according to their comprehension level of exchange programs, whereas Decision Tree-based classification pinpoints the dominant factors that shape student awareness. A total of 446 students from diverse higher education institutions participated in the study by completing a structured questionnaire. The clustering analysis reveals that 47.31% of respondents have a general familiarity with exchange programs yet lack detailed knowledge of specific requirements, whereas 30.94% exhibit an overall limited awareness. Based on these findings, promotional strategies for exchange programs should be differentiated according to students' academic progression. Moreover, the data-driven framework introduced in this study holds potential for broader application across various educational settings to strengthen the impact of academic initiatives.

Keywords:

Multi-Criteria Decision Analysis (MCDA), Analytical Hierarchy Process (AHP), classification, clustering

References

A. Kristin Bjørnnes, A. Torbjørnsen, B. Sigridur Anna Thordardottir, A. Lund, O. Johannes Hovland, and L. Skeie Skarpaas, "Exploring intentions: factors influencing international study decisions in healthcare bachelor degree programs," BMC Medical Education, vol. 25, no. 1, Apr. 2025, Art. no. 555.

O. Octaria, D. Manongga, A. Iriani, H. D. Purnomo, and I. Setyawan, "Mining Opinion Based on Tweets about Student Exchange with Tweepy and TextBlob," in 2022 9th International Conference on Information Technology, Computer, and Electrical Engineering, Semarang, Indonesia, 2022, pp. 102–106.

O. Octaria, K. D. Hartomo, I. Sembiring, H. D. Purnomo, A. Iriani, and E. Sediyono, "Analysis Perceptions Regarding Student Exchange Using Simple Random Sampling and Analytical Hierarchy Process (AHP) Methods," in 2022 9th International Conference on Electrical Engineering, Computer Science and Informatics, Jakarta, Indonesia, 2022, pp. 331–336.

K. Nyamsuren, Z. Gankhuyag, J. Ganbaatar, and N. Marinescu, "The Importance of Studying Abroad for a Sustainable Education: Research on Mongolian Student Opinions," Sustainability, vol. 16, no. 14, July 2024, Art. no. 6137.

A. Dutt, M. A. Ismail, and T. Herawan, "A Systematic Review on Educational Data Mining," IEEE Access, vol. 5, pp. 15991–16005, 2017.

J. J. H. Liou, M.-H. Chang, H.-W. Lo, and M.-H. Hsu, "Application of an MCDM model with data mining techniques for green supplier evaluation and selection," Applied Soft Computing, vol. 109, Sept. 2021, Art. no. 107534.

N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim, "How to conduct a bibliometric analysis: An overview and guidelines," Journal of Business Research, vol. 133, pp. 285–296, Sept. 2021.

A. Umam and B. Santosa, Data mining dan big data analytics, 2nd ed. Yogyakarta, Indonesia: Penebar Media Pustaka, 2018.

F. Alotaibi, "Analyzing the effects of data mining techniques on management decision making and information exchange in the industrial sector: the role of cooperation as a moderating factor in Saudi Arabia," International Journal of Data and Network Science, vol. 7, no. 4, pp. 1789–1796, 2023.

M. Dean, A Practical Guide to Multi-Criteria Analysis. London, UK: University College London, 2022.

N. M. Alshahrani, M. L. M. Kiah, B. B. Zaidan, A. H. Alamoodi, and A. Saif, "A Review of Smart Contract Blockchain Based on Multi-Criteria Analysis: Challenges and Motivations," Computers, Materials & Continua, vol. 75, no. 2, pp. 2833–2858, Mar. 2023.

G. Ferla, B. Mura, S. Falasco, P. Caputo, and A. Matarazzo, "Multi-Criteria Decision Analysis (MCDA) for sustainability assessment in food sector. A systematic literature review on methods, indicators and tools," Science of The Total Environment, vol. 946, Oct. 2024, Art. no. 174235.

D. Grondin et al., "Long-term energy scenario ranking with MCDA analysis: The case of Reunion Island," Smart Energy, vol. 17, Feb. 2025, Art. no. 100171.

A. Veljić, D. Viduka, L. Ilić, D. Karabasevic, A. Šijan, and M. Papić, "Sustainable Decision-Making in Higher Education: An AHP-NWA Framework for Evaluating Learning Management Systems," Sustainability, vol. 17, no. 22, Nov. 2025, , Art. no. 10130.

M. Maral and A. Özdemir, "A systematic review on multi-criteria decision-making methods in educational research," British Educational Research Journal, vol. 51, no. 6, pp. 3071–3106, Dec. 2025.

J. H. Ccatamayo-Barrios et al., "Comparative Analysis of AHP and TOPSIS Multi-Criteria Decision-Making Methods for Mining Method Selection," Mathematical Modelling of Engineering Problems, vol. 10, no. 5, pp. 1665–1674, Oct. 2023.

M. Rahman, "Comparative Analysis of AHP and TOPSIS Methods in Retail Business Location Selection Decision Support System," Journal Electrical and Computer Experiences, vol. 2, no. 2, pp. 52–57, Oct. 2024.

Mardiani, Ermatita, Samsuryadi, and Abdiansah, "SECI Model Design with a Combination of Data Mining and Data Science in Transfer of Knowledge of College Graduates' Competencies," International Journal of Advanced Computer Science and Applications, vol. 14, no. 7, pp. 323–329, July 2023.

M. A. Alsuwaiket, A. H. Blasi, and K. Altarawneh, "Refining Student Marks based on Enrolled Modules' Assessment Methods using Data Mining Techniques," Engineering, Technology & Applied Science Research, vol. 10, no. 1, pp. 5205–5210, Feb. 2020.

B. Altındiş and F. Bayram, "Data Mining Implementations for Determining Root Causes and Precautions of Occupational Accidents in Underground Hard Coal Mining," Safety and Health at Work, vol. 15, no. 4, pp. 427–434, Dec. 2024.

M. Rahimian and B. Panahi, "Next generation sequencing-based transcriptome data mining for virus identification and characterization: Review on recent progress and prospects," Journal of Clinical Virology Plus, vol. 4, no. 4, Nov. 2024, Art. no. 100194.

M. Azmi, A. A. Putra, D. Vionanda, and A. Salma, "Comparison of the Performance of the K-Means and K-Medoids Algorithms in Grouping Regencies/Cities in Sumatera Based on Poverty Indicators," UNP Journal of Statistics and Data Science, vol. 1, no. 2, pp. 59–66, Mar. 2023.

Qomariyah and M. U. Siregar, "Comparative Study of K-Means Clustering Algorithm and K-Medoids Clustering in Student Data Clustering," JISKA (Jurnal Informatika Sunan Kalijaga), vol. 7, no. 2, pp. 91–99, May 2022.

R. Ransing and A. Gulati, "Marathi Word Sense Disambiguation through unsupervised K-Means Clustering," Engineering, Technology & Applied Science Research, vol. 15, no. 3, pp. 22837–22843, June 2025.

N. A. Maulidiyyah, T. Trimono, A. T. Damaliana, and D. A. Prasetya, "Comparison of Decision Tree and Random Forest Methods in the Classification of Diabetes Mellitus," JIKO (Jurnal Informatika dan Komputer), vol. 7, no. 2, pp. 79–87, July 2024.

A. Mohaghegh, S. Farzin, and M. V. Anaraki, "A new framework for missing data estimation and reconstruction based on the geographical input information, data mining, and multi-criteria decision-making; theory and application in missing groundwater data of Damghan Plain, Iran," Groundwater for Sustainable Development, vol. 17, May 2022, Art. no. 100767.

H. Taherdoost, "Determining Sample Size; How to Calculate Survey Sample Size," International Journal of Economics and Management Systems, vol. 2, pp. 237–239, Nov. 2017.

R. V. Krejcie and D. W. Morgan, "Determining Sample Size for Research Activities," Educational and Psychological Measurement, vol. 30, no. 3, pp. 607–610, Sept. 1970.

M. A. Memon, H. Ting, J.-H. Cheah, R. Thurasamy, F. Chuah, and T. H. Cham, "Sample Size for Survey Research: Review and Recommendations," Journal of Applied Structural Equation Modeling, vol. 4, no. 2, pp. i–xx, June 2020.

"Tertiary school enrollment in South East Asia." TheGlobalEconomy.com. https://www.theglobaleconomy.com/rankings/Tertiary_school_enrollment/South-East-Asia/.

"Python for Data Analysis, 3E - 2 Python Language Basics, IPython, and Jupyter Notebooks." WesMcKinney. https://wesmckinney.com/book/python-basics.

F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," The Journal of Machine Learning Research, vol. 12, pp. 2825–2830, Nov. 2011.

A. Karadas, D. A. Dokuzcan, and C. Çevik, "The Validity and Reliability Study of the Turkish Version of the Perceived Professional Preparedness of Senior Nursing Students' Questionnaire," Teaching and Learning in Nursing, vol. 20, no. 3, pp. e643–e650, July 2025.

D. G. Bonett and T. A. Wright, "Cronbach's alpha reliability: Interval estimation, hypothesis testing, and sample size planning," Journal of Organizational Behavior, vol. 36, no. 1, pp. 3–15, Jan. 2015.

D. J. Ketchen and C. L. Shook, "The Application of Cluster Analysis in Strategic Management Research: An Analysis and Critique," Strategic Management Journal, vol. 17, no. 6, pp. 441–458, June 1996.

T. L. Saaty and L. G. Vargas, Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. Boston, MA, USA: Springer US, 2012.

R. Zhao et al., "Decision tree based parameter identification and state estimation: Application to Reactor Operation Digital Twin," Nuclear Engineering and Technology, vol. 57, no. 7, July 2025, Art. no. 103527.