Simulation of trends in the use of e-payment using agent based models


  • Elanjati Worldailmi Universitas Islam Indonesia
  • Ismianti Ismianti Universitas Pembangunan Nasional Veteran Yogyakarta



Agent Based, Epayment, Cashless


Bank Indonesia (BI) as the central bank in Indonesia has launched a movement to use non-cash instruments in conducting transactions on economic activities. The majority of Indonesian people are increasingly ready to trade without cash or cashless society. The country's economic policy factors, the availability of various non-cash payments, and online sales and purchases, encourage the tendency to use non-cash transactions (e-payment). One way to find out these trends is to use a model. Models can help understand and explain real phenomena more easily and efficiently than directly observing. One model that can be used is Agent Based Modeling and Simulation (ABMS). By using ABMS, the development of models with complex behaviors, dependencies, and interactions can be developed more easily. ABMS is able to describe processes, phenomena, and situations. In this study, the factors that influence the tendency to use e-payment are obtained from various references. From these factors, then created a scenario as a sub-purpose of this model. In simulations using ABMS, detailed descriptions explained based on ODD Protocol elements can be more easily understood and complete. ODD systematically evaluates a model. The advantage is that ODD can improve the accuracy of model formulas and make the theoretical basis more visible.


Borshchev, A. & Filippov, A. (2004). From System Dynamics and Discrete Event to Practical Agent-Based Modeling: Reasons, Techniques, Tools. Proceedings of the 22nd International Conference of the System Dynamics Society, Oxford, England.

Dehbini, N., Birjandi, M., & Birjandi, H. (2015). Factors Influencing the Adoption of Electronic Payment Cards in Urban Micro-Payments. Research Journal of Finance and Accounting,16(1), 2015.

Fahmi, S.C. (2016). Analysis of Factors Affecting Community Preferences Using Cash Transactions (Case Study of 5 College Students in Yogyakarta). Scientific Journal of Economic Study Program FEB UMY.

Fajri, M. (2018). Simulation with Agent-Based Modelling, Accessed 16 Oktober 2019.

Gilbert, N. (2007). Agent-Based Models. University of Surrey: The Centre for Research in Social Simulation,

Grimm, V., Berger, U., DeAngelis, D.L., Polhill, J.G., Giske, J., & Railsback, S.F. (2010). The ODD Protocol, A Review and First Update. Ecological Modelling 221 (2010) 2760-2768.

Junadi & Sfenrianto. (2015). A Model of Factors Influencing Consumer’s Intention to Use E-Payment System in Indonesia. Procedia Computer Science 59 (2015) 214-220.

Macal, C.M. & North, M.J. (2008). Agent-Based Modeling and Simulation: ABMS Example. Proceedings of the 2008 Winter Simulation Conference.

Nisa, D.D. Ariyani, T.S., & Oktaviani, K. (2013). Analysis of Factors Affecting Customers Using Mandiri Internet Banking Services. Journal of Management Vol 13 No 1 November 2013.

Rogers, E. (1995). Diffusions of Innovations, Accessed 30 Mei 2017.

Sitohang, E.T. (2016). Determinants That Influence Use of Internet Banking Services. Mediterranean Journal of Social Sciences, 3(5), 33-41.

Wilensky, U. & Rand, W. (2015). An Introduction to Agent-Based Modeling. Cambridge, Massachusetts: The MIT Press.

Yudhistira P., A. (2014). Analysis of Factors Influencing Preference and Accessibility to the Use of Electronic Payment Cards. Scientific Journal of the Department of Economics Faculty of Economics and Business, Universitas Brawijaya Malang.




How to Cite

Worldailmi, E., & Ismianti, I. (2020). Simulation of trends in the use of e-payment using agent based models. International Journal of Industrial Optimization, 1(1), 29–42.