%L eprintsunpak8884
%A Ristina Eka Salsabila
%A Arie Qur’ania
%A Siska Andriani
%X Application of Data Mining for Predicting Business Actors' Income Using
Monte Carlo and Autoregressive Integrated Moving Average (ARIMA)
Ristina Eka Salsabila1
, Arie Qur’ania2
, Siska Andriani3*
1,2,3 Department of Computer Science, Faculty of Mathematics and Natural Science, Pakuan
University, Bogor, West Java, 16143, Indonesia
Abstract
The rapid advancement of technology today, it is almost certain that all tasks can be optimized through
technology, especially for predicting future figures. Considering the swift growth of Warung Bakso Berkah
Wonogiri, optimal management is crucial. This study aims to estimate future sales revenue at Warung Bakso
Berkah Wonogiri 3 by comparing two methods: Monte Carlo and ARIMA. The prediction process will involve
collecting historical data (such as sales from two years ago) and projecting it into the future using the Monte
Carlo method and Autoregressive Integrated Moving Average (ARIMA). This research utilizes revenue data
over a period of two years (January 2022 to December 2023), totaling 730 data points. Using the Monte Carlo
method, revenue predictions are based on random numbers, while ARIMA predictions rely on the best
predictive modeling process. The model produced by the Monte Carlo method has an error value of 0.07% or
an accuracy of 99.93%, whereas the model generated using ARIMA has an error value of 0.03% or an accuracy
of 99.97%. The process and results of this study are visualized through a graph for easier understanding and
to illustrate annual comparisons..
Keywords: Prediction, Sales Revenue, Python, Monte Carlo, Autoregressive Integrated Moving
Average (ARIMA)
%I Universitas Pakuan
%D 2024
%T Application of Data Mining for Predicting Business Actors' Income Using
Monte Carlo and Autoregressive Integrated Moving Average (ARIMA)