Modeling and Simulation of Service Turnaround Time Using a Simple Linear Regression Method on a Discrete Service System

Juwita Sari

doi:10.59934/jaiea.v5i2.2045

Authors

Juwita Sari Universitas Islam Negeri Sumatera Utara

DOI:

https://doi.org/10.59934/jaiea.v5i2.2045

Keywords:

Modeling and Simulation, Simple Linear Regression, Discrete Service System, Service Time Prediction

Abstract

Modeling and simulation are important approaches in discrete service system analysis to understand the behavior of systems as well as estimate their operational performance. One of the main problems in service systems is the uncertainty of service completion time which is affected by various operational parameters. This study aims to build a computational model in predicting service completion time using a simple linear regression method as a mathematical approach based on historical data. The independent variable used represents the system load, while the dependent variable is the duration of service completion. The linear regression model is constructed through a mathematical modeling process and simulated using actual data to generate an estimated service time. The simulation results were then analyzed using prediction error metrics, such as Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE), to evaluate the model's performance. The results show that simple linear regression is able to provide a consistent and fairly representative service time estimate in discrete service systems with low complexity. This approach can be used as a basis for operational decision-making as well as a starting model for the development of more complex computing-based prediction systems.

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References

S. Nurmilawati, H. Windyatri, and G. H. Pradipto, "Analysis and Optimization of Queues at Bank X Cikarang Using Discrete and 5S Event Simulation Methods," vol. 4, no. 2, pp. 4561–4570, 2025, doi: https://doi.org/10.31004/riggs.v4i2.1102.

N. Manurung et al., "Analysis of queue data in fast food restaurant services using linear regression," pp. 1846–1851.

S. N. Putri, "Queuing and Simulation Models to Improve User Experience in Online Services," 2025, doi: https://doi.org/10.64847/sistematik.v2i1.108.

D. S. Nugroho, E. H. Deliana, Y. Ismiawati, S. Zaiima, J. N. No, and J. Tengah, "Discrete Modeling and Simulation of the Queue System at Trans Semarang Bus Stops as an Evaluation of Customer Traffic and Service Capacity," vol. 8, no. 2, pp. 57–71, 2024.

T. Wahyudi, D. S. Arroufu, and C. Author, "IMPLEMENTATION OF DATA MINING PREDICTION DELIVERY TIME," vol. 4, no. 1, pp. 84–92, 2022.

J. Sistem and S. Computer, "PREDICTION OF RAW MATERIAL INVENTORY FOR THE PRODUCTION OF PROCESSED FOOD 'KRISPI STUDIO' USING," vol. 8, no. 2, pp. 84–94, 2023.

T. B. Kurniawan and A. Alvino, "Multiple Linear Regression for Predicting the Ship Booking Time : A Case Study at PT . Indonesian Ocean," vol. 2023, no. 1, 2023.

R. D. Astanti, MODELING AND SIMULATION OF INDUSTRIAL SYSTEMS. Andi Publishers, 2025.

A. A. Zulfa, T. Ibrahim, and O. Arifudin, "THE ROLE OF WEB-BASED ACADEMIC INFORMATION SYSTEMS," vol. 6, no. 1, pp. 115–134, 2025, doi: https://doi.org/10.57171/jt.v6i1.615.

Ek. Students, Php Uncover (Fully Uncover PHP Programming). Semarang: Prima Agus Teknik Foundation, 2021. [Online]. Available: https://penerbit.stekom.ac.id/index.php/yayasanpat/article/view/207

N. Huda, "Basic PHP: Learning Functions (1/3)," Jago Coding.

D. Primary et al., "Management of Tracking in Real Time on a Website-based Laundry," vol. 12, no. September, pp. 797–810, 2023.

W. K. Wise, Linear regression to predict the number of attendants to the number of officers in determining guard scheduling. CV. Creative Industry of the Archipelago, 2020.

D. S. Pramaningrum et al., "TRUNCATED SPLINE SEMIPARAMETRIC PATH MODELING SIMULATION IN CASHLESS SOCIETY DEVELOPMENT CASE," vol. 12, no. 1, pp. 31–38, 2025, doi: 10.25126/jtiik.2025128679.

E. Rubiani and Sriani, "Modeling and Simulation of Registration Services at Simpang Tuntungan Clinic with Monte Carlo Method," vol. 19, no. x, pp. 117–125, 2024.

A. M. A. Rusdy, "Application of Linear Regression Method to Drug Supply and Demand Prediction Case Study of Point of Sales Applications," vol. 3, no. 2, pp. 121–126, 2022.

J. Jurnal, S. Dan, T. Jsit, V. N. J. Hal, and J. Septriaznu, "Design and Build a Web-Based Property Selling Value Prediction System Using Linear Regression," vol. 4, no. 1, pp. 27–45, 2024.

N. Puspita and A. W. Utami, "Design and Build Predictions of Prospective Students on a Website-Based New Student Admission Information System Using the Linear Regression Method," vol. 04, no. 01, pp. 62–71, 2023.

J. T. Informatika and U. P. Ronggolawe, "APPLICATION OF MEAN ABSOLUTE ERROR ( MEA ) METHOD IN LINEAR REGRESSION ALGORITHM FOR RICE PRODUCTION PREDICTION," no. 1, pp. 78–83, 2019.

H. Suroyo, D. Fauzi, U. B. Darma, and S. South, "UI UX DESIGN OF WEBSITE-BASED GUEST DATA APPLICATION IN," vol. 2, no. 1, pp. 146–154, 2025