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Home > Articles

Optimalisasi Prediksi Tingkat Pendapatan Desa Berdasarkan Jenis Usaha Menggunakan Metode Monte Carlo

  • Oki Jaya Varera
    Independent Researcher


DOI: https://doi.org/10.37034/infeb.v4i1.120
Keywords: BUMDes, Income, Prediction, Monte Carlo, Simulation

Abstract

Village-Owned Enterprises or called BUMDes are basically a pillar of economic activity in the village that functions as social as well as commercial institutions. BUMDes Rawa Bento is a business entity that is not only engaged in tourism and mountain equipment rental but also provides tour guides, city tours and family gatherings. In achieving optimal results for the use of the budget, a strategy is needed to predict the use of the budget in the following year which requires data on the previous use as material for making decisions. This study aims to predict annual income. So that BUMDes can determine strategies quickly and optimally. The data processed is income data from 2019 to 2021 at the BUMDes Rawa Bento. From the problems that have been explained that village-owned enterprises need a system that is able to predict the use of future budgets, making it easier to make budgets for the future. So this study uses the Monte Carlo method to solve the problem. The Monte Carlo method is a numerical method described as a statistical simulation method. The Monte Carlo method is a method used to assist companies in solving problems, where problems can be solved with uncertainty. The data used as samples were taken randomly. The test results use the Monte Carlo method with an average income of IDR 8,344,028, an average simulation of IDR 8,914,583 and the average percentage value for predictions for 2020 and 2021 is 97%. The Monte Carlo method can accurately predict annual income. Keywords: BUMDes, Revenue, Prediction, Monte Carlo, Simulation.

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Published
2022-03-02
Issue
Vol. 4, No. 1 (March 2022)
Section
Articles
How to Cite
Varera, O. J. (2022). Optimalisasi Prediksi Tingkat Pendapatan Desa Berdasarkan Jenis Usaha Menggunakan Metode Monte Carlo. Jurnal Informatika Ekonomi Bisnis, 4(1), 23-27. https://doi.org/10.37034/infeb.v4i1.120
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