Open AccessComparative Performance of Prediction Methods for Digital Wallet Transactions in the Pandemic Period
Author(s) -
Arwin Datumaya Wahyudi Sumari,
Muhammad Bisri Musthafa,
Ngatmari,
Dimas Rossiawan Hendra Putra
Publication year - 2020
Publication title -
jurnal resti (rekayasa sistem dan teknologi informasi)
Language(s) - English
Resource type - Journals
ISSN - 2580-0760
DOI - 10.29207/resti.v4i4.2024
Subject(s) - mean absolute percentage error , computer science , moving average , scale (ratio) , pandemic , sma* , covid-19 , econometrics , operations research , economics , artificial neural network , artificial intelligence , engineering , geography , cartography , algorithm , computer vision , medicine , disease , pathology , infectious disease (medical specialty)
A pandemic situation such as Covid-19 which is still ongoing has given significant impacts to various sectors such as education, economy, tourism, and social which is in turn impacting the community at a national scale. On the other hand, the pandemic situation has also brought a positive impact on companies engaged in finance that utilizes information technology, namely digital wallets, a company that runs a market place in the digital world. In an effort to anticipate a dynamic market place, the company needs to predict the movement of transactions from time to time by building a model and performain the simulation to such model. Based on this problem, this paper presents simulations on the prediction models based on methods namely, naïve, Single Moving Average (SMA), Exponential Moving Average (EMA), combined SMA-naive methods, combined EMA-naive methods, as well as did the comparison of the best performance of every model by using Mean Absolute Percentage Error (MAPE) measurement. From the results of comparison, it is concluded that exponential moving average method delivers the best performance as prediction tool with MAPE of 23,4%.