PREDICTION OF CASH WITHDRAWAL DATA AT ATM MACHINES USING THE SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE METHOD (SARIMA)

  • Fitrinadi FMIPA ULM
  • Irwan Budiman FMIPA ULM
  • Andi Farmadi FMIPA ULM
  • Dodon Turianto Nugrahadi FMIPA ULM
  • Muhammad Itqan Mazdadi FMIPA ULM
Keywords: Data Mining, Finhacks, SARIMA, MAPE, ATM

Abstract

Abstract

Data mining is a series of processes to explore the added value of knowledge that has been unknown from a data set. Many algorithms can be used in solving a problem related to prediction or forecasting a new data value for the future based on pre-existing data. Sarima model is a model in time series analysis. The performance of the Seasonal Autoregressive Integrated Moving Average (SARIMA) method produces a suitable or good model used to predict cash withdrawal data at ATM machines. The data used in the study is a dataset of ATM transactions originating from Finhacks. The result of error using MAPE (Mean Absolute Percenttage Error) on the predicted result of cash withdrawal data at atm machines is K1 16.75%, K2 18.09%, K3 7.85%, K4 5.67%, and K5 11.80%. So it can be concluded that the data matches using the SARIMA model that has been selected because the MAPE value is smaller than 20%.

Published
2022-12-29
How to Cite
Fitrinadi, Irwan Budiman, Andi Farmadi, Dodon Turianto Nugrahadi, & Muhammad Itqan Mazdadi. (2022). PREDICTION OF CASH WITHDRAWAL DATA AT ATM MACHINES USING THE SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE METHOD (SARIMA). Journal of Data Science and Software Engineering, 3(02), 103-118. Retrieved from https://jurnalmahasiswamipa.ulm.ac.id/index.php/integer/article/view/74
Issue
Vol. 3 No. 02 (2022)
Section
Articles