Monte Carlo Method for Predicting Educational Service Revenue at Each Level of Education at PT. Kanaka Belajar

Raden Radian Baratasena; Mukidin Mukidin; Kosim Kosim; Adinda Rainah Lova Ariatin

doi:10.58421/misro.v4i3.497

DOI:

https://doi.org/10.58421/misro.v4i3.497

Authors

Raden Radian Baratasena Sekolah Tinggi Ilmu Komputer Poltek Cirebon
Mukidin Mukidin Sekolah Tinggi Ilmu Komputer Poltek Cirebon
Kosim Kosim Sekolah Tinggi Ilmu Komputer Poltek Cirebon
Adinda Rainah Lova Ariatin Sekolah Tinggi Ilmu Komputer Poltek Cirebon https://orcid.org/0009-0000-4786-3274

Keywords:

Private Tutoring, Monte Carlo, Income, Education, Prediction

Abstract

The increasing demand for education services in Indonesia has significantly influenced the growth of private tutoring businesses. PT Kanaka Belajar is a company that provides private tutoring services, yet it continues to face challenges related to revenue uncertainty and fluctuating student enrollment, which can affect financial management and increase the risk of business bankruptcy. Therefore, a reliable and accurate revenue prediction system is necessary at each level of education to estimate income for the coming year. The Monte Carlo method is a computational algorithm that uses repeated random sampling to obtain numerical results. This study applied the Monte Carlo method to forecast revenue based on historical data from 2021 to 2023. This research aims to develop a web-based revenue prediction system for educational services at different levels by implementing the Monte Carlo simulation. The results demonstrated that the model provided high prediction accuracy for private tutoring income at the elementary school level in 2023, with an MAPE value of 1.57%. The system predicted 314 tutoring sessions, while the data showed 319 sessions, resulting in a minimal difference of 5 sessions. These findings suggest that the Monte Carlo method effectively forecasts educational service revenue, where smaller percentage error values indicate higher accuracy, while larger errors suggest lower forecast reliability.

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