Peramalan Menggunakan Model Hybrid ARIMAX-NN untuk Total Transaksi Pembayaran Nontunai - Nuning Kusumaningrum; Ika Purnamasari; Meiliyani Siringoringo (Program Studi Statistika Fakultas MIPA UNM, 2023)
Non-cash payment transactions in Indonesia continue to experience an increase marked by the high consumptive behavior of the people. This consumptive behavior is based on the many attractive offers, especially on year-end holidays which are the effect of calendar variations. ARIMAX is a time series method that is able to detect the effects of calendar variations. Meanwhile, to increase the level of forecasting accuracy, it can be combined with other methods such as Neural Networks (NN). This study aims to predict the total non-cash payment transactions in Indonesia in the period January to December 2022 using the ARIMAX-NN hybrid model. Based on the forecasting results, four highly accurate models were obtained, namely the hybrid model ARIMAX(0,1,2)-NN 1 neuron, ARIMAX(0,1,2)-NN 2 neurons, ARIMAX(1,1,0)-NN 1 neurons, and ARIMAX(1,1,0)-NN 2 neurons with MAPE values ​​for each model below 5%. Based on the four models formed, the results of forecasting in the period January to December 2022 as a whole the data tends to fluctuate and has an upward trend pattern, especially in December, which is the month when year-end holidays occur.