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Journal of Computer Science and Engineering (JCSE)
Published by Institute of Computer Science and Engineering
ISSN : -     EISSN : 27210251     DOI : https://doi.org/10.36596/jcse
Core Subject : Science,
Computer Science & IT
Computer Architecture, Processor design, operating systems, high-performance computing, parallel processing, computer networks, embedded systems, theory of computation, design and analysis of algorithms, data structures and database systems, theory of computation, design and analysis of algorithms, data structures and database systems, artificial intelligence, machine learning, data science, Information System
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 61 Documents
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Monte Carlo Simulation Application for Meteorological Parameter Prediction Rizqi, Muhammad Nur; Sugiarto, Cahya; Afifah, Ghaitsa
Journal of Computer Science and Engineering (JCSE) Vol 6, No 2: August (2025)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

Rainfall in Indonesia, particularly in southern coastal regions such as Cilacap Regency, is strongly influenced by the interaction of multiple meteorological variables. This study aims to predict monthly meteorological parameters consisting of rainfall, air temperature, wind speed, humidity, and solar radiation intensity using the Monte Carlo simulation method based on historical data from 2022 to 2024 obtained from the Tunggul Wulung Cilacap Class III Meteorological Station. The simulation process involved probability distribution fitting and random number generation for 10,000 iterations for each parameter. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE). The results show that air temperature and humidity achieved the highest predictive accuracy, with MAPE values of 4.04 percent and 3.18 percent. These values indicate high model consistency. Solar radiation intensity and wind speed produced moderate accuracy with MAPE values of 38.83 percent and 44.44 percent. In contrast, rainfall exhibited low predictive performance with a MAPE of 53.13 percent. This low performance is primarily caused by high temporal variability and limited data length. The findings demonstrate that Monte Carlo simulation is effective for predicting meteorological variables with stable patterns but less suitable for parameters with extreme fluctuations such as rainfall 

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