Prediction Model of Batam University Management Information System Based on Regression and Machine Learning
Abstract
The implementation of Management Information Systems (MIS) in higher education is strongly influenced by the use of information technology (IT) and the digital competence of its users. This study aims to analyze the influence of IT use and digital competence on MIS at Batam University, as well as to develop the best prediction model by comparing conventional statistical methods and machine learning algorithms. Research data were collected through a Likert-scale-based questionnaire from respondents selected using a purposive sampling technique. Data analysis was performed using a combination of Multiple Linear Regression, Random Forest Regressor, and Gradient Boosting Regressor. The results of statistical tests indicate that the use of IT and digital competence simultaneously have a positive and significant effect on MIS with a coefficient of determination ($R^2$) of 87.40%. On the other hand, the results of the machine learning model evaluation show that Random Forest Regressor provides the best performance with the lowest MAE and RMSE values, and the highest prediction accuracy ($R^2$ Score) reaching 0.905072. This study concludes that the integration of statistical and machine learning approaches can produce an accurate and adaptive MIS prediction model to support data-driven decision making at Batam University
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References
IR Panjaitan and MIP Nasution, “Integration of Business Information Systems with Machine Learning Technology,”J. Research. Sist. Inf., vol. 2, no. 2, 2024.
C. Janiesch and K. Heinrich, “Machine learning and deep learning,”Electron. Mark., vol. 31, no. 3, pp. 685–695, 2021.
Farhanuddin, SEK Sihombing, and Yahfizham, “Comparison of Multiple Linear Regression and Random Forest Regression in Predicting Information System Project Management Budget,”J. Comput. Digit. Bus., vol. 3, no. 2, pp. 86–97, 2024.
S. Falah, AT Jaka, and NQ Nada, “Application of linear regression and random forest algorithms to predict sales at Uchi Parfume,”J. Inform. Technol. and Science, vol. 7, no. 3, pp. 1317–1326, 2025.
R. Hidayatet al., “Implementation of random forest regression algorithm to predict product sales in supermarkets,” J. Sist. Inf. and Sist. Comput., vol. 10, no. 1, pp. 101–109, 2025.
BMA Karim, N. Pfeuffer, and O. Hinz, “Machine learning in information systems - a bibliographic review and open research issues,”Electron. Mark., vol. 31, pp. 643–670, 2021.
MQ Ramadhani and DB Arianto, "Application of Machine Learning with Logistic Regression Algorithm to Predict Poverty Levels,"J. Inf. Syst. Comput., vol. 5, no. 2, pp. 61–67, 2025.
G. Hesamian, F. Torkian, A. Johannssen, and N. Chukhrova, "Intelligent Systems with Applications A learning system-based soft multiple linear regression model,"Intel. Syst. with Appl., vol. 22, no. April, p. 200378, 2024.
HA Dolatsara, Y.-J. Chen, and LAJ Farmer, “Explaining Predictive Model Performance: An Experimental Study of Data Preparation and Model Choice,”Sage Journals, vol. 11, no. 3, p. 2021, 2021.
RI Nugraha, A. Rezha, E. Najaf, and R. Permatasari, "BCA Stock Price Prediction Using Time Series Method With GRU (Gated Recurrent Unit),"J. Technology and Open Source, vol. 8, no. 2, pp. 432–440, 2025.
MA Sembiring and FW Sembiring, “Performance Analysis of Regression Models in Machine Learning to Predict Rice Prices,”J. Inf. Syst. Informatics Eng., vol. 8, no. 1, pp. 144–152, 2024.
O. Rainio, J. Teuho, and R. Klén, “Evaluation metrics and statistical tests for machine learning,”Sci. Rep., pp. 1–14, 2024.
C. Miller, T. Portlock, DM Nyaga, and JMO Sullivan, “A review of model evaluation metrics for machine learning in genetics and genomics,”Front. Bioinform, vol. 4, no. September, pp. 1–13, 2024.
A. Dudáš and D. Demian, “Predictive analysis visualization components in simulated data streams,”Discov. Comput., vol. 27, no. 12, 2024.
A. Telea, A. Machado, and Y. Wang, "Seeing is Learning in High Dimensions : The Synergy Between Dimensionality Reduction and Machine Learning,"SN Comput. Sci., vol. 5, pp. 1–25, 2024.
P. Mishra, CM Pandey, U. Singh, A. Gupta, S. Chinmoy, and A. Keshri, “Descriptive Statistics and Normality Tests for Statistical Data,”Ann. Card. Anaesth., vol. 22, no. 1, pp. 67–72, 2019.
J. Ledolter, O. W. Gramlich, and R. H. Kardon, “Display of Data,”Invest Ophthalmol Vis Sci, vol. 61, no. 6, pp. 1–8, 2020.
Z. Liuet al., “The normality assumption on between ‑ study random effects was questionable in a considerable number of Cochrane meta ‑ analyzes,” BMC Med., vol. 21, pp. 1–12, 2023.
DI Warton, “Global Simulation Envelopes for Diagnostic Plots in Regression Models,”Am. Stat., vol. 77, no. 4, pp. 1–77, 2023.
M. Ugurlu and F. Sari, "A 3-year retrospective study of clinical durability of bulk-filled resin composite restorations,"Restor Dent Endod, vol. 47, no. 1, pp. 1–11, 2022.
R. Saltos-rivas, P. Novoa-herna, and RS Rodrıguez, "On the quality of quantitative instruments to measure digital competence in higher education : A systematic mapping study,"PLoS One, vol. 16, no. 9, pp. 1–27, 2021.
RS Sukorini, A. Marini, and RN Aulia, "New Era Of Higher Education: Digital Transformation And Information System Management,"Improv. (Scientific Journal for Improving the Quality of Education), vol. 11, no. 2, pp. 154–166, 2024.
A. Setyadi, S. Pawirosumarto, A. Damaris, and R. Dharma, "Risk management, digital technology literacy, and modern learning environments in enhancing learning innovation performance: A framework for higher education,"Educ. Inf. Technol., vol. 30, no. 11, pp. 15095–15123, 2025.
Z. Fan and R. Chiong, “Identifying digital capabilities in university courses : An automated machine learning approach,”Educ. Inf. Technol., pp. 3937–3952, 2023.
MA Quttainah and P. Singh, "Implementation of Digital Competency-building Strategy in Management Education,"Sage Journals Home Implement., vol. 42, no. 1, 2024.
TD Dang, TT Phan, TNQ Vu, TD La, and VK Pham, "Heliyon Digital competence of lecturers and its impact on student learning value in higher education,"Heliyon, vol. 10, no. 17, p. e37318, 2024.
LP Sari, A. Habibi, and R. Hendra, "Determinants of Learning Satisfaction in Technology-Integrated Higher Education: The Role of Digital Competence and Self-Regulated Learning,"Techno-Pedagogy 15, vol. 15, no. 2, 2025.
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